QGeNIe Modeler
USER MANUAL

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BayesFusion, LLC®

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Table of Contents
1.

Read me first

7

2.

Hello QGeNIe!

93.

Introduction

33

3.1

Guide to QGeNIe manual ........................................................................................... 34

3.2

QGeNIe ...................................................................................................................... 34

3.3

SMILE Engine ............................................................................................................. 35

3.4

BayesBox ................................................................................................................... 36

3.5

BayesMobile .............................................................................................................. 37

3.6

Distribution information ............................................................................................ 37

3.7

QGeNIe on a Mac ....................................................................................................... 39

3.8

QGeNIe on Linux ........................................................................................................ 41

3.9

Non-Latin Alphabets in QGeNIe ................................................................................. 41

3.10

Copyright notice ........................................................................................................ 42

3.11

Disclaimer .................................................................................................................. 42

3.12

Acknowledgments ..................................................................................................... 43

4.

Decision-theoretic modeling

45

4.1

Decision analysis ........................................................................................................ 46

4.2

Probability ................................................................................................................. 46

4.3

Bayesian networks ..................................................................................................... 47

4.4

Bayesian updating ..................................................................................................... 50

4.5

Changes in structure .................................................................................................. 50

5.

Building blocks of QGeNIe

5.1

53

Introduction .............................................................................................................. 54

5.2
QGeNIe workspace .................................................................................................... 54
5.2.1
Introduction ................................................................................................................ 54
5.2.2
The menu bar .............................................................................................................. 56
5.2.3
Graph view .................................................................................................................. 57
5.2.4
Tree view ..................................................................................................................... 72
5.2.5
Status bar .................................................................................................................... 76
5.2.6
Output window ........................................................................................................... 77
5.2.7
Help menu ................................................................................................................... 78
5.3
Components of GeNIe models .................................................................................... 79
5.3.1
Node types .................................................................................................................. 79

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5.3.2
5.3.3
5.3.4
5.3.5
5.3.6
5.3.7
5.3.8
5.4
5.4.1
5.4.2
5.4.3
5.4.4
5.4.5
5.5
5.5.1
5.5.2
5.5.3
5.5.4
5.5.5
5.5.6
5.5.7
5.5.8
5.6
5.6.1
5.6.2
5.6.3
5.7
5.7.1
5.7.2
5.7.3
5.7.4
5.7.5
5.7.6
5.8

6.

The DeMorgan gate .................................................................................................... 80
The CAST gate ............................................................................................................. 95
Submodels ................................................................................................................... 97
Arcs ............................................................................................................................ 106
Node status icons ...................................................................................................... 107
Text boxes ................................................................................................................. 109
Annotations ............................................................................................................... 110
Model and component properties ............................................................................ 112
Network properties ................................................................................................... 112
Submodel properties ................................................................................................ 117
Tools menu and Standard toolbar ............................................................................ 119
Node properties ........................................................................................................ 121
Arc properties ........................................................................................................... 128
Visual appearance, layout, and navigation ............................................................... 128
Introduction .............................................................................................................. 128
Viewing nodes in the Graph View ............................................................................ 129
Zooming and full screen mode ................................................................................. 132
Format toolbar and Layout menu ............................................................................ 134
Graph layout functions ............................................................................................. 138
Selection of model elements .................................................................................... 140
Model navigation tools ............................................................................................. 145
In-degree analysis ..................................................................................................... 147
Saving and loading models in QGeNIe ...................................................................... 148
Introduction .............................................................................................................. 148
File menu ................................................................................................................... 155
QDSL file format ........................................................................................................ 160
Inference algorithms ................................................................................................ 160
Introduction .............................................................................................................. 160
Node menu ............................................................................................................... 161
Network menu .......................................................................................................... 162
Clustering algorithm ................................................................................................. 164
Relevance-based decomposition .............................................................................. 164
EPIS Sampling ............................................................................................................ 164
Keyboard shortcuts .................................................................................................. 164

Using QGeNIe

167

6.1

Introduction ............................................................................................................ 168

6.2

Applications of qualitative probabilistic modeling .................................................... 168

6.3
Bayesian networks ................................................................................................... 170
6.3.1
Building a qualitative Bayesian network .................................................................. 170

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6.3.2
Simple interaction patterns ...................................................................................... 170
6.3.3
Entering and retracting evidence ............................................................................. 172
6.3.4
Controlling values ..................................................................................................... 175
6.3.5
Viewing results .......................................................................................................... 179
6.3.6
Structural analysis ..................................................................................................... 182
6.3.7
Case Manager ........................................................................................................... 199
6.4
Most Effective Actions calculation ............................................................................ 202
6.4.1
Introduction .............................................................................................................. 202
6.4.2
Enabling Most Effective Actions calculation ............................................................ 203
6.4.3
Most Effective Actions calculation ........................................................................... 205
6.5
Dynamic Bayesian networks ..................................................................................... 208
6.5.1
Introduction .............................................................................................................. 208
6.5.2
Creating DBN ............................................................................................................. 209
6.5.3
Inference in DBNs ..................................................................................................... 215

7.

Resources

229

7.1

Books ...................................................................................................................... 230

7.2

Research papers ....................................................................................................... 230

7.3

Conferences ............................................................................................................. 231

7.4

Model repository ..................................................................................................... 231

7.5

Social Media ............................................................................................................ 232

7.6

References ............................................................................................................... 232

Index

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Read me first8

Read me first
1

Read me first
Welcome to QGeNIe manual, Version 4.1.R2, Built on 4/27/2024.
This manual is available in CHM, PDF and HTML formats, all available athttps://support.bayesfusion.com/docs/.
CHM version of QGeNIe manual is also distributed with QGeNIe.
If you are new to QGeNIe and would like to start with an informal, tutorial-like introduction, pleasestart with the Hello QGeNIe! section. If you are an advanced user, please browse through the Table ofContents or search for the topic of your interest.

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2

Hello QGeNIe!
This section offers an informal introduction to QGeNIe, similar to the light introduction to the C
programming language offered by Brian Kerninghan and Dennis Ritchie in their milestone book (seeKernighan & Ritchie, 1988). We will show you how to create a simple qualitative Bayesian networkmodel, how to save and load it, and how to use it to answer simple questions. Once you have madeyourself familiar with QGeNIe in this informal way, you can proceed with the Elements of QGeNIe
chapter, which offers a thorough introduction to various elements of QGeNIe.
QGeNIe is an interactive program that allows for rapid creation of causal models in uncertain
domains. These models represent propositions by means of nodes, which always take two possible
values: True and False. Even though the underlying methodology is quantitative and grounded inprobability theory, models created in QGeNIe use no numbers and rather qualitative sliders, colors,
and gradients. Degrees of truth of propositions are represented by colors. Mathematically speaking,they represent the probability of the state True (or False - it is the users' choice). While QGeNIe users
can define the color scale themselves, the default color scale is a range between red and green,
representing undesirable and desirable states. QGeNIe allows for an interactive exploration of themodels, examining the effects of observations and manipulations of individual variables. The focus ofreasoning in QGeNIe are not exact probabilities but rather sign and order of magnitude effects that canbe perceived by means of colors.
We will demonstrate the basic functionality of QGeNIe on a simple model that will describe
relationship among four variables: High Productivity, Hot Weather, Climate Control in the Building
and Work To Do. Each of these variables is propositional and can take two states: True and False.
Through this modeling effort, we would like to create a model that will answer the following questions:
What is the expected impact of failure of the climate control in the building on productivity on a hot
day and on a normal day respectively? We would also like to obtain the conditional probability table
for the variable High Productivity for a further use and refinement in GeNIe. While this example
contains only four variables, it illustrates all basic concepts, which once understood can be used inbuilding more complex models. Please keep in mind that the functionality covered in this section
merely touches what you can do with QGeNIe and gives you just a taste of qualitative Bayesianmodeling.
If you have not already started QGeNIe, please start it now.

A. Let us create the nodes that will represent the variables in our model.
The Tool Menu shows a list of model elements that you can create. These are also displayed as buttons
on the Standard Toolbar.

Select Node button () from the Standard Toolbar or Tool Menu.

The Node button will become recessed and the cursor will change to an arrow with an ellipse in bottom
right corner. Move the mouse to a clear portion of the screen inside QGeNIe window (the main model
window is called the Graph View) and click the left mouse button. You will see a new node appearing
on the screen as shown below:

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The small squares around the node indicate that the node is selected. The most recently created node
is automatically selected. You can also select any node by clicking on it. You can change the size of theselected node by dragging the small squares.
Once you have drawn the node on the Graph View, the Node button on the toolbar will become normal
again and the Select Objects button will become recessed.
QGeNIe allows you to name nodes. Names are simply strings of any characters and any length.
QGeNIe assigned the node that you have just created the name Node1. QGeNIe also placed the newly
created node's name in Edit mode immediately, so you can enter a more descriptive name. In this case,we want to name it High Productivity.

All QGeNIe variables are binary and should refer to some proposition. The truth of this proposition
will be the subject of inference in QGeNIe. It is a good heuristic to think of these propositions as
desirable and undesirable. It is best to assign variable names that are meaningful and self-explanatory.
Please note that QGeNIe does not put any limitations on the length of the names. When working withthe model, posterior probabilities will be displayed by means of colors. Typically, one assigns green
color to desirable propositions and red color to undesirable ones. Red has been found to draw user's
attention and we advise that it be used to undesirable propositions. QGeNIe allows its user to define anode coloring scheme. When defining this scheme, it is a good idea to follow the meaning of the
majority of nodes. Single nodes can be designated as having the reverse meaning and the coloring
scheme will be reversed for them.
Similarly, we add nodes for the variables Hot Weather, Climate Control in the Building and Work To
Do. It is worth mentioning here that if you want to draw multiple model elements of the same type,then you can avoid having to select the node button again and again by double-clicking on a Standard
Toolbar button instead of single-clicking it the first time. This will place you in "sticky mode," in which
the tool button stays recessed and you can draw multiple elements of that type. You can return tonormal mode by clicking on the Select Objects button () or clicking on the recessed button again.
After adding the three nodes, we enter their names, resize them to look pretty and display the entire
name, and move them to their destination positions using mouse drag and drop functionality. Here iswhat the effect of this operation could look like:

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B. Let us specify the causal connections between the nodes.
In order to represent the fact that each of the three nodes (Hot Weather, Climate Control in the
Building and Work To Do) influences the node High Productivity, we will add influence arcs to themodel.
Click on the Arc ( ) tool (note that the cursor changes), then click on the Hot Weather node, hold theleft mouse button and drag the mouse to the node High Productivity, and release the button anywhere
within the new node.
GeNIe will draw an arc from Hot Weather node to High Productivity and display the following dialog:

which allows you for specifying the type of causal relationship between Hot Weather and High
Productivity. There are four possible types of causal relationships: Causes, Barriers, Requirements,

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and Inhibitors. Causes make their effects more likely, i.e., have a positive influence on their effects.
Barriers have a negative effect. Requirements are necessary for the effects to happen. Inhibitors, when
true, make the effect impossible. Each of these four types of relationships is imperfect, so the effectmay happen even if the requirement is not present and may happen even if the requirement is absent.
Similarly, an inhibitor may be imperfect and the effect may take place even if it is present or not takeplace even if it is absent.
In case of the causal relationship between Hot Weather and High Productivity, it is fair to assume that
it is a Barrier - hot weather will tend to decrease productivity. We choose Barrier thus. Similarly, wedraw arcs from Climate Control in the Building and Work To Do to High Productivity, making them a
Cause (climate control should increase productivity) and a Requirement (existence of work to be done
is a necessary condition for productivity) respectively. We obtain the following directed graph:

Please note that the heads of the arrows are all different, indicating the types of relationships between
a cause and an effect.

C. Setting the desirable state for each node.
QGeNIe uses colors to show desirable and undesirable states. By default, these are the states True but
this can be changed for each node individually. In our model, the state True of each of the variables isdesirable, except for the node Hot Weather. Let us change the desirable state for node Hot Weather toFalse. To do that, we right-click on the node and choose Node properties:

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Within node's properties, please check the Reverse colors check box:

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This will cause the node colors to follow the state False. Zero probability of Hot Weather will be shownas green and probability one will be shown as red.

D. Now, let us define the numerical properties of the model, i.e., nodes and the interactions among
them.
There are two types of parameters in QGeNIe: (1) Node beliefs (probabilities), and (2) Interaction
probabilities.
Node probabilities express either the prior probability of the proposition expressed by the node (forthose nodes that have no parents in the model graph) or leak, which is the probability of the
proposition when each of the node's parents is inactive. To change this probability for any node, justdouble-click on the node. This will result in dimming every node in the model except for Hot Weather
and displaying a slider for changing the prior belief (prior probability) of the proposition Hot Weather:

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The prior probability of (degree of belief in) the proposition can be adjusted by moving the slider. You
can also move the slides by finer steps (single percentiles) using the horizontal arrows on yourkeyboard. Let us move it to the value of 80%, which means that there is a prior probability of 80% of
hot weather. As we move the slider, the color of the node changes. We repeat this for the node Climate
Control in the Building:stating that there is 90% chance that climate control is operating in the building and for the node
Work To Do:

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stating that 95% of the time there is work to do.
For any node that has parents in the graph, we specify two parameters: The Leak and the interaction
probabilities. We will explain the meaning of these probabilities in section The DeMorgan gate inthis manual. For now, we ask your patience and trust in the sound probabilistic meaning of theseparameters. The only node with parents in this simple model is High Productivity. Double-clicking on
this node invokes the slider for the Leak probability.

The Leak expresses the probability that High Productivity is going to be True when all of its parents
are in their inactive states, i.e., the weather is going to be hot, there is going to be no climate control inthe building, and there is going to be work to do, but no other unmodeled causal factors influencing
productivity are present. Please note that the user interface to the elicitation shows you this situation
by fixing the values of Hot Weather to True (T), Climate Control in the Building to False (F), andWork To Do to True (T). We estimate this probability to be 10%.

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E. We will now specify the strength of influences of the different causal factors of High Productivity.
Interaction probabilities, also called Weights, generally describe the strengths of causes in their ability
to influence the effect. The weight means something slightly different, depending on the type ofinteraction. Every variable in the model has a so-called "distinguished state," which is the state thatexerts no influence on its children. For example, the distinguished state of Hot Weather is False, as hot
weather will decrease productivity but its absence, i.e., mild weather, will have no influence on
productivity. The distinguished state of Climate Control in the Building is False, as presence of climate
control will influence productivity. The distinguished state of Work to Do is True, as having work to dois the normal situation that will not influence productivity. To learn more about the meaning of
weights in each case, please look at the The DeMorgan gate section of this manual.
In order to change any of the interaction probabilities, just double-click on the corresponding arc.

The weight of the influence can be adjusted by moving the slider. Weight of 80%, as pictured above,
represents in case of a barrier, which is the relationship between Hot Weather and High Productivity,
the probability that the productivity is going to be high if the weather is not hot, there is no climate
control in the building and there is work to do. QGeNIe's user interface to the elicitation helps you withthis by fixing the values of Hot Weather to False (F), Climate Control in the Building to False (F), andWork To Do to True (T). Please note that only the variable Hot Weather is not in its distinguished state
when estimating this probability.
The Weight of the relationship between Climate Control in the Building and High Productivity of 80%(see the image below) means that there is 80% chance that the productivity is going to be high if theweather is hot, the climate control is present in the building, and there is work to do. QGeNIe's userinterface to the elicitation helps you with this by fixing the values of Hot Weather to True (T), Climate
Control in the Building to True (T), and Work To Do to True (T). Please note that only the variable
Climate Control in the Building is not in its distinguished state when estimating this probability.

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The Weight of the relationship between Work To Do and High Productivity of 99% (see the image
below) means that there is 99% chance that the productivity is going to be low if there is no work to do,
the weather is not hot and there is climate control in the building. QGeNIe's user interface to theelicitation helps you with this by fixing the values of Hot Weather to True (T), Climate Control in the
Building to True (T), and Work To Do to False (F). In this case, none of the variables are in their
distinguished states, as the relationship of a requirement with causes and barriers follows the AND
function (more about this in the The DeMorgan gate section of this manual.

There is one more setting that we will perform - we will designate one of the nodes, High Productivity,
as the focus of our analysis. Usefulness of this setting will become clear later in this section. To
designate the High Productivity node as the focus of inference, please right-click on it and select Set
Focus.

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Let us pause for a moment and examine the graphical structure of our completed model:

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The structure shows causal interactions among the variables. We can see that hot weather, climate
control, and availability of work to do all impact productivity. The influence of individual variables
differs qualitatively. This is pictured by different arrowheads. Climate control impacts productivity
positively, hot weather negatively, and work to do is a necessary condition for productivity to be high.
Colors of nodes reflect probabilities of the propositions that they represent. Generally, green color
denotes probabilities that are within an acceptable range, red color denotes probabilities that are
worrisome. Small circles on the top of each of the node help in judging the distance of the node color
from the extremes (colors representing probabilities zero and one). Please note that one half of eachcircle is white and the other shows the color of the extreme. And so, Hot Weather node's probability isclearly on the side of red, between 0.5 (white color) and dark red (probability 1.0) shown by the otherhalf of the small circle. The probability of Climate Control in the Building is quite close to 1.0 - please
note that there is little difference between the node's color and the green half of the small circle.
There is one node (High Productivity) that is special and designated as the focus of reasoning (we will
return to this in Section H). Each node has two small icons in its lower-right corner. These icons allow
for setting an observation of the node and also show the observed value.
The model structure can be analyzed additionally by displaying the links between nodes (the arcs) invariable width, corresponding to the magnitude of their influence. To enable variable arc widths, press
the Enable variable arc widths (QGeNIe Modeler) tool. This results in the following view:

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Please note that the thickness of each arc represents the strength with which the parent nodeinfluences the child node. It is useful for analyzing the critical paths of information flow.

F. At this point you should save your work.
1. Click on Save button () on the Standard Toolbar.

GeNIe will display the Save As.. dialog shown below:

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2. Enter Productivity as the File name and click on Save.

G. Now let us put our model to work and answer the question posed in the beginning of this tutorial.
To answer the question "What is the expected impact on productivity of failure of the climate control inthe building on a hot day and on a normal day respectively?", you will need to tell QGeNIe that you
have observed the Climate Control in the Building to be False. There are two ways of achieving this.
The first, slightly more cumbersome, is to right-click on the node Climate Control in the Building andchoose Set Evidence/False from the context menu that will pop up.

There are two shortcuts for observing a node's value: (1) through double-clicking on one of the small
square icons () in the lower-right corner of each node, or (2) through pressing the letter T or F (forTrue and False respectively). In this case, we double-click on the icon False ( ) of the Hot Weather
node, which makes the icon bold or press F once the node has been selected. Please note that every
time that we make an observation of a node value, the colors of model nodes change. Colors represent
probabilities of the selected states and are meant to draw your attention. We observe the impact of thetwo observation made (please note the bold observation icons in the picture below) on the node High
Productivity.

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The colors indicate that a failing climate control will not have much of an impact when the day is not
hot. Please note the general idea of showing colors in QGeNIe. When looking at the model, we can seethat there is one variable that is a potential problem -- Climate Control in the Building, as its color isdeep red. The other nodes and our focus variable, High Productivity in particular, seem to be fine andshow as green. Let us change the observation for the node Hot Weather to True by double-clicking on
the corresponding observation icon T. We can see in the image below that there are now three nodes
that are in red: Climate Control in the Building, Hot Weather and High Productivity. Hot weather in
combination with failing climate control in the building cause problems with productivity.

Will fixing the climate control help? We can check this by setting the Climate Control in the Building
to be True by double-clicking on the corresponding observation icon and observe that the node High
Productivity turns somewhat green again.

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It is possible to see the exact numerical posterior probabilities of any of the propositions. For that,please right-click on the node in question and select Node Properties:

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Please choose the Value tab in the property sheets,

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which will look as follows:

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We can see that the numerical posterior probability of High Productivity being True is equal to0.77941.

H. Value of observation and value of manipulation.
QGeNIe allows for calculating the value of observation and the value of manipulation based on ameasure known as cross-entropy. Cross-entropy, or expected change in entropy, is a unit-less measureof the expected change in entropy of a focus variable in the model. In order to calculate the value of
information, the model has to contain exactly one focus variable. We have set the focus variable to beHigh Productivity in Section E above. Please note that the node is marked by a target icon ( ).

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Every node in the model can be marked as Observable and Manipulable by checking a corresponding
check box in the node's property sheet.

Setting a node as Observable means that it is possible to observe its value (True or False), setting anode as manipulable means that it is possible to impose a value on it by means of an externalintervention (this value is also True or False). We can, thus, observe the state of the climate control
system or we can set its state (by turning it on or off, breaking it, or fixing it when broken). The

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difference between observation and manipulation is subtle but quite clear. It is possible, for example,
to observe whether a day is hot but it may be impossible to make it hot. In case of climate control
system, we can both observe its value and manipulate it. Once the model nodes have been marked as
Observable or Manipulable, we can let QGeNIe calculate the value of observing any of the observablenodes and the value of manipulating any of the manipulable nodes from the point of view of learning
more about the Focus variable (in this case, variable High Productivity). To invoke this calculation,please press the Most Effective Actions window icon (pane.

). This will open the following window

The Observations tab shows the list of all observable variables rank-ordered from the mostinformative to the least informative in learning the probability of the focus variable (High Productivity
in our case). The numerical values are unit-less and express the expected change in entropy of the
focus variable. It looks like observing whether there is enough work to do is the most informative forlearning about high productivity.
Clicking on the Manipulations tab shows the list of all manipulable variables rank-ordered from themost effective to the least effective in changing the probability of the focus variable:

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Please note that the list does not contain the variable Hot Weather, as it was not marked as
Manipulable. It looks like making sure that there is enough work to do is the most effective way of
increasing productivity.

What we have created is a simple qualitative causal model. You can create more complex models in asimilar way.
You can find the above model named Productivity.qdsl in the Qualitative Models directory among
other example models that come with QGeNIe or download it directly from our model repository.

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Introduction
3

Introduction

3.1

Guide to QGeNIe manual
The best user interface to a computer program is one for which there is no need for a manual. This has
been from the very start our design motto and, from the compliments of our users, we learn that wehave (almost) reached our objective. Most of the tasks in QGeNIe are intuitive and conform to thegeneral standards of well designed user interfaces. If you do not know how to do something, just try
what you would find most intuitive and and what you would do in a well designed modern userinterface. There is a good chance that this will work for you.
This manual may turn out to be superfluous for many users. Still, there are many ways in which people
learn and some users will find it useful. Some may find it handy to use the search functionality in casethey encounter a problem. Others, familiar with the methodology, may want to read through thesection Using QGeNIe, which describes various QGeNIe modules. Yet others, may want to the treat
this manual as a simple handbook for decision-theoretic modeling.
Decision-theoretic modeling is not as easy as writing a brief document, preparing a slide show, or
drawing a picture. While there are many good books available that cover decision-theoretic modeling,
they typically use theoretical concepts and draw models symbolically on paper. Building them insoftware is different. Besides, much of QGeNIe functionality is novel and not described or otherwise
covered anywhere. This is the main reason why even QGeNIe needs an easily accessible manual,tutorial-style introductions, and on-line help.
QGeNIe manual, the introduction of which you are reading at the moment, is available for a variety ofplatforms: HTML, compiled HTML (CHM), PDF, etc., all available fromhttps://support.bayesfusion.com/docs/.

3.2

QGeNIeQGeNIe is an interactive development environment for rapid creation of qualitative causal models in
uncertain domains. These models represent propositions by means of nodes in an acyclic directed
graph. These nodes are always propositional and take two possible values: True and False. The colorsof these nodes represent the degrees of truth of the propositions. Mathematically speaking, the colors
represent the probability of the state True (or False - it is the users' choice). While QGeNIe users candefine the color scale, the default is a range between red and green, representing undesirable anddesirable states respectively. QGeNIe allows for an interactive exploration of the models, examining
the effects of observations and manipulations of individual variables.
There are two major applications of QGeNIe:
1. A standalone system for rapid creation of causal models, useful in all kinds of strategic planningproblems, where problems are complex enough to be a challenge for an unaided human mind and,at the same time, too complex to model by means of fully specified, precise quantitative models.
For sufficiently complex problems, it is a challenge for an unaided human mind to predict effects ofvarious actions. Typically, in addition to obvious consequences of a decision, there will be indirect
pathways through which actions may propagate through the system and lead to surprising effects.
QGeNIe is a tool for capturing the knowledge and intuitions of decision makers and focusing group
discussion on calculating the global effects of various decision options. QGeNIe offers what can becalled an instant gratification system in the sense of showing interactively the effects ofobservations and manipulations.

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2. A system for generating simple first-cut version of quantitative probabilistic models. Models
developed by means of QGeNIe can be exported to GeNIe and refined into fully quantitativemodels.
An early version of QGeNIe was created and developed at the Decision Systems Laboratory, Universityof Pittsburgh between 1995 and 2015. In 2015, we created a company, BayesFusion, LLC, and acquireda license for QGeNIe from the University of Pittsburgh. Continuing the tradition of the Decision
Systems Laboratory, we are making it available free of charge to the academic community for researchand teaching use in order to promote decision-theoretic methods in decision support systems. QGeNIe
has been tested extensively in many teaching, research, and commercial environments. We arecontinuously improving it and are interested in user comments. We encourage the users of QGeNIe to
let us know about encountered problems and possible suggestions.
GeNIe's name and its uncommon capitalization originates from the name Graphical Network
Interface, given to the original simple graphical user interface to SMILE, our library of classes forgraphical probabilistic and decision-theoretic models. Its successor was GeNIe, SMILE's GUI. QGeNIe
is a simplified, qualitative graphical user interface to SMILE.
QGeNIe allows for building models of any size and complexity, limited only by the capacity of theoperating memory of your computer. QGeNIe is a modeling environment. Models developed using
QGeNIe can be exported to GeNIe or embedded into any applications and run on any computing
platform, using SMILE, which is fully portable.
QGeNIe, GeNIe and SMILE have been originally developed to be major teaching and research tools inacademic environments and have been used at hundreds if not thousands of universities world-wide.
Most research conducted at the Decision Systems Laboratory, University of Pittsburgh, found its way
into both programs. Because of their versatility and reliability, QGeNIe, GeNIe and SMILE havebecome incredibly popular and became de facto standards in academia, while being embraced by many
government, military, and commercial users.
The strongest element of GeNIe (and indirectly of QGeNIe), one that distinguishes it from a large
number of other graphical modeling tools, is its user interface. We have paid a lot of attention to it andit shows. While developing decision-theoretic models takes typically an enormous amount of time,
GeNIe cuts the effort by orders of magnitude and it will lead to a fast return of the investment in its
licensing fees. SMILE is not far behind and belongs to the easiest to learn and use, most reliable, andfastest libraries for graphical models.

3.3

SMILE Engine
SMILE (Structural Modeling, Inference, and Learning Engine) is a fully platform independent libraryof functions implementing graphical probabilistic and decision-theoretic models, such as Bayesian
networks, influence diagrams, and structural equation models. Its individual functions, defined in
SMILE Applications Programmer Interface (API), allow to create, edit, save, and load graphicalmodels, and use them for probabilistic reasoning and decision making under uncertainty.
SMILE is implemented in C++ in a platform independent fashion. We also provide Java(jSMILE), .NET (SMILE.NET), .COM (SMILE.COM), Python (PySMILE) and R (rSMILE) wrappers forusers who want to use SMILE with languages other than C++. Through the Java wrapper, SMILE canbe used in programming environments such as Matlab or Ruby. Through the .NET wrapper, it can be

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Introductionused, among others, from C# and VB.NET. SMILE is equipped with an outer shell, a developer's
environment for building graphical decision models, known as GeNIe, or a qualitative graphical userinterface, known as QGeNIe. GeNIe and QGeNIe are platform dependent and run only on Windows
computers, although our users have successfully run them under MacOS and Linux operating systems.
SMILE can be embedded in programs that use graphical probabilistic models as their reasoning
engines. Such programs can be distributed to end users or placed on servers for cloud use. Models
developed in SMILE can be equipped with a user interface that suits the user of the resulting
application most.
QGeNIe, GeNIe and SMILE have been originally developed to be major teaching and research tools inacademic environments and have been used at hundreds of universities world-wide. Most research
conducted at the Decision Systems Laboratory, University of Pittsburgh, found its way into QGeNIe,
GeNIe and SMILE. Because of their versatility and reliability, QGeNIe, GeNIe and SMILE have become
incredibly popular and became de facto standards in academia, while being embraced by many
government, military and commercial users.
The strongest element of SMILE, one that distinguishes it from a large number of other graphicalmodeling tools, is its ease of use from a programmer's perspective (it offers a modern object-orientedAPI), availability for multiple platforms, its reliability (it has been tested heavily in practical researchand commercial applications since 1998), and speed (it has done very nicely in speed competitionsorganized by UAI, the annual Conference on Uncertainty in Artificial Intelligence). Speed especially is
crucial, as most calculations in probabilistic graphical models are exponential in nature.

3.4

BayesBox
BayesBox is a member of BayesFusion’s family of products developed for modeling, learning, anddecision support. It facilitates unlimited online sharing of probabilistic graphical models (such as
Bayesian networks, qualitative Bayesian networks, influence diagrams, dynamic Bayesian networks,
and hybrid Bayesian networks) created for internal or public use. Customers can upload to therepository any number of models organized into any number of categories. This repository can beaccessed by an unlimited number of users through web browsers.
Server part of BayesBox runs on Linux or Windows and is available as a software installable and fully
controlled by customers on-premises, or hosted as a service by BayesFusion. Users access BayesBox
models through any standards-compliant web browser, can explore the model structure, enter
evidence, and examine results.
Bayesian inference in the BayesBox server is performed by SMILE Engine, our cross-platform Bayesian
software library.
BayesBox can be fine-tuned to user needs and reflect customer brand, including a name, a logo, and acolor scheme. Optionally, administrator interface allows for enabling access control through a login
page.
GeNIe allows for opening models from an existing BayesBox instance, which makes BayesBox a useful
tool for teams working on decision-theoretic methodologies.
BayesFusion’s public model repository is powered by BayesBox (see https://repo.bayesfusion.com/).
For demonstration purposes, we have also created a BayesBox-based web site of a fictitious company
Evidentious, Inc., at https://demo.bayesfusion.com/.

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3.5

BayesMobile
BayesMobile is a member of BayesFusion’s family of products developed for modeling, learning, anddecision support. It runs on Apple mobile devices, such as iPhone or iPad and allows for working withdiagnostic models. We have created it for the convenience of those users who want to quickly field
their diagnostic models. More information about BayesMobile can be found our our web page.

3.6

Distribution information
Hardware and software requirements
Disk space
Full installation of QGeNIe requires less than 20 MB of disk space.
MemoryQGeNIe has practically no minimum memory requirements and can run under a minimum
Windows configuration. The actual memory requirement will depend on the size and complexity ofthe models that you create. Too little memory may result in decreased performance. In general,
conditional probability tables grow exponentially with the number of parents of a node. The
maximum number of parents of a node will, therefore, determine memory requirements. In
addition, memory requirements of the clustering algorithm grow with the connectivity of the
network.
Operating system
QGeNIe is written for the Windows operating systems. Installation of QGeNIe under Windows
operating systems may require administrator privileges. While we cannot guarantee 100%
compatibility, we are verifying with each build that it runs on macOS (formerly OS X) and Linux
under Wine. Please see QGeNIe on a Mac section for more information on running QGeNIe on amacOS. Getting QGeNIe to run under Linux is virtually identical.

QGeNIe version
To determine the version of QGeNIe that you have installed, select About QGeNIe from the Help
Menu. The version number is listed in the small frame of the following window:

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Example Networks
QGeNIe installation creates a directory named Qualitative Models, containing the following
models (all of these models are available for download from BayesFusion's interactive model
repository through File menu):
Engine.xdsl
The Engine models several causes of a possible failure of starting a car engine and illustrates theinteraction of Causes and Barriers in the DeMorgan gate.
Fever.xdsl
The Fever model illustrates the interaction of Causes, Barriers, Requirements and Inhibitors in
the DeMorgan gate.

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Fire.xdsl
The Fire model has been developed for the purpose of explaining the four types of causal
interactions defined in the DeMorgan gate that is at the foundations of QGeNIe models. The model
has first appeared in (Druzdzel 2010).
Pregnancy.xdsl
The Pregnancy model illustrates working of a Requirement in the DeMorgan gate.
Product Temporal.xdsl
The Product Temporal model is a qualitative dynamic Bayesian network (DBN), illustrating higher
order temporal influences.
Productivity.xdsl
The Productivity model is a simple qualitative network used in the Hello QGeNIe section of thismanual and it models various causal factors in productivity.
Project.xdsl
The Project model illustrates a causal graph for group brainstorming about various project
obstacles and results from the point of view of an airplane manufacturer.
Stability.xdsl
The Stability model has been developed by Bradd C. Hayes & Jeffrey I. Sands and published in:
Bradd C. Hayes & Jeffrey I. Sands, "Doing Windows: Non Traditional Military Responses toComplex Emergencies." DSD Research Report 97-1, Decision Support Department, Center forNaval Warfare Studies, U.S. Naval War College. Figure 5-1, page 101.

3.7

QGeNIe on a Mac
Running QGeNIe on macOS (formerly Mac OS X) with Wine.
1. Download and install Wine on your Mac. Follow the instructions at Wine website(https://wiki.winehq.org/MacOSX).
2. Download QGeNIe on your Mac. Double-click on QGeNIe Installer icon to start the setup wizard.
3. After installing QGeNIe, run Wine from the Launchpad. This opens the Terminal window
configured to run Windows programs. Do not try to launch QGeNIe by locating its icon and doubleclicking on it.
4. In Wine's terminal, use the cd command to navigate to QGeNIe's installation directory. Assumingthat QGeNIe was installed in its default location, the command is:

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cd ".wine/drive_c/Program Files/QGeNIe 4.1"
Note that quotes are required, because some of the directory names may contain spaces.
QGeNIe Academic installs to "GeNIe 4.1 Academic" by default.
5. After changing the directory, run the qgenie_on_wine.sh script:./qgenie_on_wine.shThis starts QGeNIe using an appropriate Wine configuration.
See the screen shot below, illustrating steps 3-5.

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3.8

QGeNIe on LinuxGeNIe can be used on a Linux system with VM-based solution like VirtualBox, or with Wine (acompatibility layer capable of running Windows applications). See the QGeNIe on a Mac section for
more info about QGeNIe and Wine.

3.9

Non-Latin Alphabets in QGeNIeQGeNIe accommodates non-Latin alphabets - any text within a model can be written using lettersoutside of the Latin alphabet. This includes variables IDs, names, comments, etc.

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3.10 Copyright notice
Copyright (C) BayesFusion, LLC, under license from the University of Pittsburgh. All rights reserved.
No part of this manual may be reproduced or transmitted in any form or by any means, electronic ormechanical, without an explicit written permission of BayesFusion, LLC.
We would like to acknowledge the following trademarks:
macOS and OS X are registered trademarks of Apple, Inc.
QGeNIe, GeNIe, SMILE, BayesBox and BayesMobile and all accompanying graphics and manuals are
copyrighted (1996-2022) by University of Pittsburgh, used under license by BayesFusion, LLC, andcannot be copied or distributed without permission. The only legal way of obtaining the programs isdirectly from BayesFusion, LLC. We require that interested individuals contact us directly or visit our
web site for the most recent copy of the programs. This ensures the quality and completeness of the
programs and accompanying manuals. It also allows us to keep track of who is using the programs andto notify the users about updates and new releases.

Academic use of QGeNIe, GeNIe and SMILE
We support teachers using QGeNIe in their classes and maintain shareware resources, such as network
repositories, that are useful in teaching. (Please, visit BayesFusion, LLC web site for more
information.) QGeNIe, GeNIe and SMILE are also useful in academic research projects. Our softwareis free for academic teaching and research use. In return for this, to get credit for our work, we ask thatall publications of research or applications in which QGeNIe, GeNIe or SMILE were used contain an
explicit acknowledgment to that effect. Examples of simple acknowledgments are listed below:
The models described in this paper were created using QGeNIe, available free of charge foracademic research and teaching use from BayesFusion, LLC, https://www.bayesfusion.com/.
The core of our implementation is based on the SMILE reasoning engine for graphicalprobabilistic models, available free of charge for academic research and teaching use from
BayesFusion, LLC, https://www.bayesfusion.com/.

3.11 Disclaimer
QGeNIe, GeNIe and SMILE are made available on an "as is" basis. We have performed extensive testsof the software, which has been used in hundreds of research, teaching, and commercial projects, but
we are not providing any guarantees as to its correct working and take no responsibility for effects ofpossible errors. We do appreciate suggestions and bug reports and will do the best within ourcapabilities to correct errors and accommodate users' needs in our future development plans. If you
have suggestions or have discovered a bug in the program, please send us electronic mail at
support@bayesfusion.com.
Similarly, while we have taken much care in writing this manual and making it as accurate as possible,
we assume no responsibility for possible errors that it may contain. We encourage the readers to send
us their corrections and suggestions at support@bayesfusion.com.

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3.12 Acknowledgments
Support for the development QGeNIe, GeNIe and SMILE at the University of Pittsburgh was provided
in part by the Air Force Office of Scientific Research under grants F49620-97-1-0225 and F49620-001-0122, by the National Science Foundation under Faculty Early Career Development (CAREER)
Program, grant IRI-9624629, by Hughes Raytheon Laboratories, Malibu, California, by ARPA's
Computer Aided Education and Training Initiative under grant N66001-95-C-8367, and by the
University of Pittsburgh Central Development Fund.
While little of the original code has remained and most of the programs have been rewritten with time,
the principal developers of QGeNIe, GeNIe and SMILE (listed alphabetically) included:
Saeed Amizadeh, Steve Birnie, Jeroen J.J. Bogers, Girish Chavan, Hanyang Chen, Jian Cheng, Denver
H. Dash, Martijn de Jongh, Marek J. Druzdzel, Daniel Garcia Sanchez, Nancy Jackson, Randy Jagt,
Joost Koiter, Marcin Kozniewski, Hans van Leijen, Yan Lin, Tsai-Ching Lu, Paul Maaskant, Agnieszka
Onisko, Hans Ove Ringstad, Tomek Sowinski, Carl P.R. Thijssen, Miguel Tjon Kon Fat, Daniel
Tomalesky, Mark Voortman, Changhe Yuan, Haiqin Wang and Adam Zagorecki.
We would like to acknowledge contributions of the following individuals (listed alphabetically) tocoding, documentation, graphics, web site, and testing of SMILE and GeNIe: Kimberly Batch, Avneet
S. Chatha, Cristina Conati, Roger Flynn, Abigail Gertner, Charles E. Grindle, Christopher Hall,
Christopher A. Geary, William Hogan, Susan E. Holden, Margaret (Peggie) Hopkins, Jun Hu, Kent Ma,
Robert (Chas) Murray, Zhendong Niu, Shih-Chueh (Sejo) Pan, Bharti Rai, Michael S. Rissman, Luiz E.
Sant'Anna, Jeromy A. Smith, Jiwu Tao, Kurt VanLehn, Martin van Velsen, Anders Weinstein, David
Weitz, Zaijiang Yuan, Jie Xu and many others.
Students in the courses Decision Analysis and Decision Support Systems at the University ofPittsburgh, Decision Support Systems for Public Managers at Carnegie Mellon University andDecision Support and Expert Systems at the University of Alaska, Anchorage provided us with usefulfeedback and suggestions.
QGeNIe embeds a number of good ideas that we have gratefully assimilated over time from othersoftware, whether decision-theoretic or not. The great user interface of Analytica has been an
inspiration and a role model for us. Analytica's user interface has been developed by Max Henrion and
Brian Arnold at Carnegie Mellon University in late 1980s and early 1990s. Our treatment of submodels
is very similar to that in Analytica.

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Decision-theoretic modeling
4

Decision-theoretic modeling

4.1

Decision analysis
Decision analysis is the art and practice of decision theory, an axiomatic theory prescribing how
decisions should be made. Decision analysis is based on the premise that humans are reasonably
capable of framing a decision problem, listing possible decision options, determining relevant factors,
and quantifying uncertainty and preferences, but are rather weak in combining this information into arational decision.
Decision analysis comes with a set of empirically tested tools for framing decisions, structuring
decision problems, quantifying uncertainty and preferences, discovering those factors in a decisionmodel that are critical for the decision, and computing the value of information that reduces
uncertainty. Probability theory and decision theory supply tools for combining observations andoptimizing decisions. While QGeNIe is under continuous development, it already implements a large
set of these tools.
While decision analysis is based on two quantitative theories, probability theory and decision theory,
its foundations are qualitative and based on axioms of rational choice. The purpose of decision analysis
is to gain insight into a decision and not to obtain a recommendation. The users of QGeNIe will noticethat this important premise is reflected in its functionality and, most importantly, its user interface.

4.2

Probability
Decision theoretic and decision analytic methods quantify uncertainty by probability. It is quite
important for a decision modeler to understand the meaning of probability. There are threefundamental interpretations of probability:· Frequentist interpretation

Probability of an event in this view is defined as the limiting frequency of occurrence of thisevent in an infinite number of trials. For example, the probability of heads in a single coin tossis the proportion of heads in an infinite number of coin tosses.
· Propensity interpretation

Probability of an event in this view is determined by physical, objective properties of the object
or the process generating the event. For example, the probability of heads in a single coin toss isdetermined by the physical properties of the coin, such as its flat symmetric shape and its two
sides.
· Subjectivist interpretation

The frequentist and the propensity views of probability are known as objectivist as they assume
that the probability is an objective property of the physical world. In the subjectivist, also
known as Bayesian interpretation, probability of an event is subjective to personal measure of
the belief in that event occurring.
While the above three interpretations of probability are theoretical and subject to discussions andcontroversies in the domain of philosophy, they have serious implications on the practice of decision
analysis. The first two views, known collectively as objectivist, are impractical for most real world
decision problems. In the frequentist view, in order for a probability to be a meaningful measure of

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Decision-theoretic modelinguncertainty, it is necessary that we deal with a process that is or at least can be imagined as repetitivein nature. While coin tosses provide such a process, uncertainty related to nuclear war is a rather hardcase - there have been no nuclear wars in the past and even their repetition is rather hard to imagine.
Obviously, for a sufficiently complex process, such as circumstances leading to a nuclear war, it is noteasy to make an argument based on physical considerations. The subjectivist view gives us a tool for
dealing with such problems and is the view embraced by decision analysis.
The subjectivist view interprets probability as a measure of personal belief. It is legitimate in this view
to believe that the probability of heads in a single coin toss is 0.3, just as it is legitimate to believe that
it is 0.5 as long as one does not violate the axioms of probability, such as one stating that the sum of
probabilities of an event and its complement is equal to 1.0. It is also legitimate to put a measure of
uncertainty on the event of nuclear war. Furthermore, this measure, a personal belief in the event, canvary among various individuals. While this sounds perhaps like a little too much freedom, this view
comes with a rule for updating probability in light of new observations, known as Bayes theorem.
There exist limits theorems that prove that if Bayes theorem is used for updating the degree of belief,
this degree of belief will converge to the limiting frequency regardless of the actual value of the initialdegree of belief (as long as it is not extreme in the sense of being exactly, or infinitesimally close to, 0.0
or 1.0). While these theorems give guarantees in the infinity, a reasonable prior belief will lead to amuch faster convergence.
The subjectivist view makes it natural to combine frequency data with expert judgment. Numericalprobabilities can be extracted from databases, can be based on expert judgment, or a combination of
both. Obtaining numbers for probabilistic and decision-theoretic models is not really difficult. The
process of measuring the degree of belief is referred to as a probability assessment. Various decisionanalytic methods are available for probability assessment.

4.3

Bayesian networks
Bayesian networks (also called belief networks, Bayesian belief networks, Bayes nets, causal
probabilistic networks, or causal networks) (Pearl 1988) are acyclic directed graphs in which nodes
represent random variables and arcs represent direct probabilistic dependences among them. The
structure of a Bayesian network is a graphical, qualitative illustration of the interactions among the setof variables that it models. The structure of the directed graph can mimic the causal structure of themodeled domain, although this is not necessary. When the structure is causal, it gives a useful,modular insight into the interactions among the variables and allows for prediction of effects ofexternal intervention.
The name Bayesian originates from the fact that the joint probability distribution represented by aBayesian network is subjective (please recall that they are sometimes called belief networks; Bayesian
approach is often used as a synonym for subjective view on probability) and this subjective probability
distribution can be updated in the light of new evidence using Bayes theorem.
The purely theoretical view that the structure of a Bayesian network represents independences andthat lack of an arc between any two variables X and Y represents a (possibly conditional) independence
between them, is not intuitive and convenient in practice. A popular, slightly informal view of Bayesian
networks is that they represent causal graphs in which every arc represents a direct causal influence
between the variables that it connects. A directed arc from X to Y captures the knowledge that X is acausal factor for Y. While this view is informal and it is easy to construct mathematically correct
counter-examples, it is convenient and widely used by almost everybody applying Bayesian networks
in practice. There is a well-established assumption, with no convincing counter-examples, that causal

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Decision-theoretic modelinggraphs will automatically lead to correct patterns of independences. Lack of arcs between pairs ofvariables expresses simple facts about absence of causal influences between them. Independences
between these pairs of variables follow from the structure of the graph. It is, thus, possible to construct
Bayesian networks based purely on our understanding of causal relations between variables in ourmodel.
Both, the structure and the numerical parameters of a Bayesian network, can be elicited from anexpert. They can also be learned from data, as the structure of a Bayesian network is simply arepresentation of independences in the data and the numbers are a representation of the joint
probability distributions, which can be inferred from the data. Finally, both the structure and thenumerical probabilities can be based on a mixture of expert knowledge, measurements, and objective
frequency data.
Structural properties of Bayesian networks, along with their numerical parameters allow for
probabilistic reasoning within the model. Probabilistic reasoning within a BN is induced by observing
evidence. A node that has been observed is called an evidence node. Observed nodes become
instantiated, which means, in the simplest case, that their outcome is known with certainty. The
impact of the evidence can be propagated through the network, modifying the probability distributionof other nodes that are probabilistically related to the evidence. For example, the network constructedin section Hello QGeNIe! allows for calculating the probability of high productivity (shown by color)
given observations of hot weather and broken climate control device.

This calculation amounts at the foundations to a repetitive application of Bayes theorem in order toupdate the probability distributions of all nodes in the network. Different ways of applying Bayes
theorem and different order of updating lead to different algorithms. Essentially, the existingalgorithms for reasoning in Bayesian networks can be divided into three groups: message passing,graph reduction, and stochastic simulation. Explicit representation of independences allows for an
increased computational tractability of probabilistic reasoning. Probabilistic inference in singlyconnected BNs is very efficient. Unfortunately, exact algorithms for multiply connected networks areliable to exponential complexity in the number of nodes in the network. Cooper (1990) has shown thatthe problem is NP-hard in general. Still, efficient software, like SMILE that is embedded into QGeNIe,
offers reasonable computing times even in networks consisting of thousands of nodes.
The following network (Project) models various considerations faced by an airplane manufacturer

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Observed Increased Load, Higher Speed Requirements, and Personnel Shortages lead to light green
Negative Profit. A Bayesian network model allows for calculating the most informative future
observations and actions. The following graph shows the most informative observations for the modeland the situation pictured above.

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Embedding Bayesian networks technology into user programs
Bayesian networks can be embedded into custom programs and web interfaces, helping withcalculating the relevance of observations and making decisions. SMILE Engine, our software libraryembedding Bayesian networks, has been deployed in a variety of environments, including custom
programs, web servers, and on-board computers.

4.4

Bayesian updating
Bayesian networks allow for performing Bayesian inference, i.e., computing the impact of observing
values of a subset of the model variables on the probability distribution over the remaining variables.
For example, observing a set of symptoms, captured as variables in a medical diagnostic model, allows
for computing the probabilities of diseases captured by this model.
Bayesian updating, also referred to as belief updating, or somewhat less precisely as probabilistic
inference, is based on the numerical parameters captured in the model. The structure of the model, i.e.,an explicit statement of independences in the domain, helps in making the algorithms for Bayesian
updating more efficient. All algorithms for Bayesian updating are based on a theorem proposed by Rev.
Thomas Bayes (1702-1761) and known as Bayes theorem.
Belief updating in Bayesian networks is computationally complex. In the worst case, belief updating
algorithms are NP-hard (Cooper 1990). There exist several efficient algorithms, however, that makebelief updating in graphs consisting of tens or hundreds of variables tractable. Pearl (1986) developed
a message-passing scheme that updates the probability distributions for each node in a Bayesian
networks in response to observations of one or more variables. Lauritzen and Spiegelhalter (1988),
Jensen et al.(1990), and Dawid (1992) proposed an efficient algorithm that first transforms a Bayesian
network into a tree where each node in the tree corresponds to a subset of variables in the originalgraph. The algorithm then exploits several mathematical properties of this tree to perform
probabilistic inference.
Several approximate algorithms based on stochastic sampling have been developed. Of these, best
known are probabilistic logic sampling (Henrion 1998), likelihood sampling (Shachter & Peot 1990,
Fung & Chang 1990), backward sampling (Fung & del Favero 1994), Adaptive Importance Sampling(AIS) (Cheng & Druzdzel 2000), and quite likely the best stochastic sampling algorithm available at the
moment, Evidence Pre-propagation Importance Sampling (EPIS) (Yuan & Druzdzel 2003). We
believe that the EPIS algorithm is currently the state of the art algorithm for Bayesian networks andhave included it in both QGeNIe and GeNIe. Approximate belief updating in Bayesian networks has
been also shown to be worst-case NP-hard (Dagum & Luby 1993).
In most practical networks of the size of tens or hundreds of nodes, Bayesian updating is rapid andtakes between a fraction of a second and a few seconds.

4.5

Changes in structure
Changes in structure are external interventions that modify a system in question. An example of achange in structure is imposition of a new tax within the economic system of a country. It is of criticalinterest to decision makers to be able to make predictions of the effects of changes in structure. Since
the model reflects the reality in its unmanipulated form and changes in structure of the kind

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contemplated by the decision maker have perhaps never been performed, predicting their effect is in
general daunting.
In order to be able to predict the effect of arbitrary changes in structure, it is necessary that the model
contain causal information. Directed graphs allow for representation of causality. One may adopt theconvention that each arc in the graph denotes a direct causal relation between the parent and the childnode. We recommend that all models built are causal in that sense. The operation of controlling avalue is an example of a causal manipulation and may result in changes in structure.
QGeNIe, GeNIe and SMILE are unique in supporting changes in structure in decision models. Please
see Controlling values of variables section of this document for additional details.

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5

Building blocks of QGeNIe

5.1

Introduction
This section of QGeNIe documentation reviews elements of QGeNIe that form building blocks to its
functional modules. We will often refer to the following terms, so we will define them briefly:

Property sheets
Property sheets are used to define the properties of the networks, nodes and submodels used.
Each element has its own property sheet.

Menus
Menus are collections of possible actions and option switches, typically present on the top of themain program window.

Pop-up menus
Pop-up menus are menus that appear on the screen when the user right clicks on any element in
QGeNIe. Sometimes pop-up menus appear when clicked upon in a dialog window.

Toolbars
Toolbars are used for quick access to frequently used commands. All the commands found on the
toolbar can be typically found in one of the menus.

Dialogs
Dialogs are usually small windows containing descriptions of features that can be interactively
modified.

Keyboard shortcuts
Many commands in QGeNIe can be executed by pressing a sequence of keys. We will report
shortcuts for many of the commonly used operations but will also provide a section summarizing
all QGeNIe shortcuts at the end of this chapter.

5.2

QGeNIe workspace

5.2.1

Introduction
QGeNIe workspace is what you see and use when you work with QGeNIe. Its main goal is to allow you
to view the network under development in many alternative ways, which we call views. QGeNIe
window looks as follows:

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Building blocks of QGeNIeThe fundamental views that most users work with are the Graph View and the Tree View.
Status Bar displays the focus node and the number of evidence nodes that are set for the active model.
The Output Window is where QGeNIe will display important messages for you.
The tool bars and menus, along with the property sheets for nodes, submodels and networks aredescribed in the Building Blocks of QGeNIe section.
QGeNIe allows for working with multiple models at the same time. Each model can have an unlimited
number of arbitrarily nested submodels. Each model and submodel can be viewed simultaneously in
the Graph View and the Tree View.
At any given moment, only one window in QGeNIe workspace can be active. The active window can beeasily recognized by both being completely in the foreground and by its distinct characteristic (such asa darker blue top bar) determined by your Windows settings. To make a window active just click on
any of its elements. An alternative way of making a window active, useful when the workspace contains
many windows, is by selecting its name on the Window menu.

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Building blocks of QGeNIeThe Window menu displays a list of all currently open windows. A check mark appears in front of thename of the active window. The user can select any of the windows in the bottom part of the Window
menu to be active. To select a window, select its name and release the mouse button.
The Window menu offers commands that help in arranging multiple views of multiple documents inthe application window:
Cascade command arranges all windows in an overlapping fashion with their Title bars clearly visible.
The Tile Horizontally and Tile Vertically commands arrange windows into non-overlapping tiles
either horizontally or vertically (depending on the option selected), allocating equal space to eachwindow.
Arrange icons arranges icons of all minimized windows at the bottom of the main QGeNIe window.
Please note that if there is an open window that covers the bottom of the main window, then it may
cover some or all of the icons and they may not be visible.

5.2.2

The menu bar
The Menu Bar is displayed at the top of the QGeNIe window and displays menu headings. Clicking ona menu heading will open the menu and display a list of commands under that menu. You can click on
a command name to choose that command. The most frequently used commands are also displayed as
tool bars (collections of buttons under a common theme). The menus that are available depend on
what is open in the workspace. QGeNIe has two different menu bars:(1) when no model is open,(2) when a model is open in the workspace,

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Building blocks of QGeNIeThe difference between the two has to do with the fact that most of the menus apply to an open model.
Here is a brief introduction to each of the menus. Details for each of the menus are covered in various
sections of this manual.
File Menu has commands for creating, opening, saving, closing, and printing a model. It also allows forexporting QGeNIe models to GeNIe and exporting all textual comments and annotations from a model
to a text file.
Edit Menu has commands for cutting, copying and pasting elements of the model, searching for anelement, selecting multiple elements, as well as highlighting model elements.
View Menu has commands for viewing and hiding various toolbars and Status Bar, selecting format oflabeling for nodes, zooming the graphical representation of models.
Tools Menu has commands for selecting various drawing tools for model construction.
Network Menu has commands for displaying network properties, clearing all evidence, locating the
focus node if there is one defined, selecting the updating algorithm, and operating on qualitative
dynamic Bayesian network models.
Node Menu has commands for displaying Node properties, setting and clearing evidence, controlling
node value, setting the Focus, clearing evidence, selecting view type of the node, locating relations of
the node, and showing its connections to the rest of the model.
Layout Menu has commands to adjust grid properties and layout options for the nodes.
Window Menu has commands to arrange the open windows in GeNIe and to switch between windows.
Help Menu has commands to display online help and modify help settings.
Note: Menu items that are grayed out either do not apply to the current selection or are unavailable.

5.2.3

Graph view
The Graph View is the primary model view in QGeNIe. It shows a directed graph in which each node
represents a variable and each arc represents an influence between two nodes. It is an intuitiveenvironment for creating and editing networks, useful in gaining insight into models by making thestructure of their graphs explicit. A slightly modified version of the Graph View is the Cost Graph
View, described in a separate section.
An example of the graph view is shown below:

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Graph View can be enhanced dramatically by structuring the model hierarchically into submodels.
Please see the section on QGeNIe submodels to learn more about it.
The Layout Menu and buttons on the Format Toolbar can be used to change the aesthetic propertiesof the Graph View.
Commands for displaying or hiding the grid and aligning the elements in the graph can be found in the
Layout Menu.
The Format Toolbar has buttons for changing the font, color and size of the labels of the nodes, andbuttons for performing the aligning operations on text and on the elements of the graph.
Please see Layout Menu and Format Toolbar for more information.

Opening a Graph View window

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Building blocks of QGeNIeThe graph view window is always open by default whenever a new model is opened or created. It is alarge sheet with variables placed at user-designated locations. You can select the Open option from theFile Menu to open a saved network file. You can create a new network by selecting the New option
from the File Menu.

Closing a Graph View window
There are three ways in which you can close a Graph View window:· By clicking on the Close () button at the top right of the Graph View window.
· By selecting the Close option in the File Menu.
· By selecting the Close Network option from the Network Pop-up menu in the Tree View.

If you close all the windows (the main window and all sub-model windows) of an open network then it
will result in closing the file, and if any changes have been made on the network, QGeNIe will give you
a warning with the dialog box shown below.

You can save the changes by clicking on the Yes button. Click on Cancel to continue working on the
network.

Working with models in the Graph View:
Each network is opened in a separate graph view sheet in the workspace. Double clicking on a clear
area of the graph view sheet will open the Network Property Sheet. Right clicking on any clear area of
the graph view sheet will display the Network Pop-up menu, which can be used to modify various
properties of the network.

Working with nodes in the Graph View:
You can draw new nodes in the Graph View by selecting the appropriate tool from the Tool Menu or
clicking on the appropriate button on the Standard Toolbar.
Double clicking on any node will open its Node Properties Sheet. Right clicking on the node will
display the Node Pop-up menu. It can be used to modify the properties of the node.

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By default, QGeNIe displays the node names within the node icons in the Graph View. If you want
QGeNIe to display node identifiers instead, select Identifiers in the View Menu to switch the display toidentifiers.

Working with submodels in the Graph View:
Double clicking on any submodel in the Graph View will open a Graph View for that submodel. You
can go back to the main network by either minimizing or closing the submodel using the buttons on
the top right of the submodel window. Right clicking on the submodel will display the Submodel Popup Menu. It can be used to modify the properties of the submodel.

Adding model elements
You can draw the following model elements in the Graph View:· Nodes· Submodels· Arcs· Text Boxes

To add any model element to the Graph View, you have to first select a tool, either from the Tools
menu below

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Building blocks of QGeNIeor a button from the Standard Toolbar below

The next step is to click on any clear area of the Graph View. For all elements except the arc, QGeNIe
will draw the icon of the element in the Graph View.
To add an arc between two nodes,
1. Select the arc tool and click on the parent node.
2. Drag the mouse cursor to the child node and release the mouse button.
QGeNIe will show the following dialog:

Clicking on any of the four tiles, representing four types of causal connections, will add an arc of thecorresponding type from the parent to the child node.
To learn more about creating nodes and arcs, See Building a Bayesian network.

Selecting, re-sizing, and moving model elements
You need to select an element to perform an operation that is specific to it.
You can select a single element by clicking on it. The element will show tracker points (small squares
around the perimeter of the selected element) that can be used to re-size it. You can re-size the element

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Building blocks of QGeNIein any direction by dragging one of these points. Arcs cannot be re-sized, as QGeNIe automaticallydraws them for you between pairs of nodes connected by arcs.
Sometimes, it may be convenient to select several nodes at a time. There are four ways of selecting
nodes in groups in the Graph View:· Rectangular selection

You can select a group of nodes by clicking on an empty area of the Graph View and dragging a
selection rectangle in any direction that you wish. Any node completely within the rectangle will
be selected. When you draw a rectangular selection with SHIFT key pressed, the selection will
be added to the existing selection. When you draw a rectangular selection with both CTRL andSHIFT pressed, the current selection status will be inverted, i.e., model elements selected will
become unselected and model elements unselected will become selected.
· Extended selection

Once you have an element, such as a node or a group of nodes or text boxes, selected, you can
add or remove individual elements from the selection by holding the SHIFT key while clicking
on them. This selection process acts as a toggle, i.e., nodes that are currently selected will be deselected.
· Group selection

You can select a specific group of nodes or all nodes in the current window by choosing SelectAll from the Edit Menu. The shortcut for this selection is CTRL+A.

· Select Nodes... dialog

Select Nodes... dialog allows for selecting nodes based on their names or IDs. We will discuss
this powerful selection tool in section Selection of model elements.
· Select Evidence Nodes

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Building blocks of QGeNIeThis command selects all nodes that have observed evidence in them. If there are no evidence
nodes in the network, this choice is dimmed.
· Select Disconnected Nodes

This command selects all nodes that are disconnected from the graph, i.e., have neither parents
no children. Typically, such nodes are left disconnected by mistake, so selecting andsubsequently highlighting them is a good way of finding them in the model, which may beotherwise challenging in sizable models. If there are no disconnected nodes in the network, thischoice is dimmed.
· Select by Type sub-menu

You can select model element by type, i.e., nodes, submodels, arcs, and text boxes in the entire
model by choosing the appropriate option from the Select by Type sub-menu shown below.

You can move a node or a group of nodes to another location by dragging it. When you press the Shift
key (you need to do it after clicking on the node or the selection, as SHIFT-Click has the meaning ofadding an object to the selection), the moving is limited to horizontal and vertical directions.
A node or a set of nodes can be also moved between submodels. To move a node to a different
submodel, drag and drop it into a submodel icon or into a submodel window.

Highlighting selected model elements

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Building blocks of QGeNIeQGeNIe allows for highlighting the selected model elements (nodes, arcs, submodels, etc.) through thecommand Highlight Selection in the Edit Menu (short-cut CTRL-L) or through pressing the Highlightselection ( ) button. The following screen shot shows a rectangular selection of model elements in
Graph View,

This results in selecting a group of nodes and arcs

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When these elements are highlighted, the Graph View changes to the following

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Pressing the ESC button or selecting Clear Highlight from the Edit Menu clears the selection.

Extending the workspace
QGeNIe is somewhat restrictive in how far the Graph View space stretches in every direction and there
is typically not too much space beyond the borders. If you want to move model elements in any
direction, please bring them close to the border and this will force QGeNIe to move the border.

Deleting model elements
To delete an element or a group of elements, select them and press the Delete key on the keyboard.
Deleting a node deletes all its incoming and outgoing arcs.

Copying, cutting, and pasting model elements

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Model elements or group of elements can be copied or cut into the Windows Clipboard, andsubsequently pasted into the same or a different Graph View window or into another application. To
invoke any of these commands, please select them from the Edit Menu or from the Standard Toolbar(shown above, buttons,, and, respectively). Nodes pasted into the same window will
preserve their incoming arcs but will lose their outgoing arcs. The reason for this is simple - the nodes
need their parents in order to preserve their definition, which is typically conditional on its parents. On
the other hand, preserving the outgoing arcs would mean that their child nodes would have double setof parents, i.e., the original nodes and their copies.
Clipboard supports multiple formats. QGeNIe stores data simultaneously in three formats:
1. Native QGeNIe format for cut, copy, and paste operations between models
2. Standard text, for example names of selected nodes and comments
3. Standard bitmap, which is used for selected objects that have a graphical component
4. Picture (enhanced metafile), used for objects that have a graphical component
Each time you invoke Copy or Cut, data in all three formats are sent to the Clipboard. When pasting
into a different application, all you need to do is select Paste special from that application's Edit menu
(e.g., in Word or PowerPoint). This should bring the dialog box with all format names. Plain Paste
command does different things in different programs - it pastes data as text into Word by default but
may paste a bitmap image in Paint.
To copy a complete model as an image, first select all elements in the model using the CTRL+A
shortcut. Then use the CTRL+C shortcut to copy the model to the clipboard. Subsequently, open the
program in which you want to paste the model image (e.g., Adobe Photoshop or MS Word) and use
Paste special and then Bitmap or (better yet!) Picture in Word. If you use Photo Editor or any graphics
editing program, Paste will paste the model image by default.

Text-based search for model elements (Find command)
Find () button from the toolbar or selecting Find from the Edit Menu (shortcut CTRL-F) invokes
the following dialog, which allows for finding model elements, such as nodes, through text search

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Building blocks of QGeNIeThe Find dialog allows for finding the text string specified in the Find what box in the names andidentifiers of all elements of the models and submodels. It will also search within annotations if the
Look in annotations check box is checked. Match case flag allows for additional customization of thesearch. Pressing the Find button starts the search. If any matches are found, they are displayed in the
dialog box and the Find button changes into the Locate button. Selecting one of the results andpressing the Locate button locates the selected node in the Graph View, centers it, and flashes threetimes. You can also locate a node by double clicking on one of the results.

Network Pop-up menu for Graph View
The Network Popup Menu for the Graph View can be accessed by right clicking on any clear area of
the Graph View. Some of the options might be disabled depending on the properties of the network
selected.

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Most of the commands found here can be also invoked from the Network Menu.
Network Properties (the default operation) opens the Network Properties sheet for the network.
Clear All Evidence is the same command as in the corresponding command in the Network Menu.
Fit to Window makes the network as large or as small as it takes to fit entirely in the Graph View.
Zoom In zooms into the network. Every application of this command increases the zoom by 25%. The
current zoom percentage is displayed on the top right of the Standard Toolbar. A similar effect can beobtained by using the zoom tool from the Standard Toolbar or the Tool Menu.
Zoom Out is the opposite of the Zoom In and it zooms out of the network. Every application of thiscommand decreases the zoom by 25%. The current zoom percentage is displayed on the top right of theStandard Toolbar.
Zoom 100% brings the model in the Graph View to its normal size.
Open Parent Submodel is enabled only if the current network in the Graph View is a submodel ofanother network. The result of this command is opening the Graph View window displaying the parent
submodel.

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Locate Color Source is active only when the Network Popup Menu is executed inside a submodel. It
finds the node that feeds its color to the submodel icon in the Graph View.
Show External Connections is a switch that helps in model navigation. When the switch is on, QGeNIeshows all nodes that are either direct predecessors (parents) or direct successors (children) of thenodes in the current submodel but are not present in the current submodel. QGeNIe will display thesenodes at the margin of the Graph View.
Locate Focus Node finds the node designated as focus.
Paste Here pastes the contents of the clipboard onto the Graph View into the exact position of themouse click that invoked the pop-up menu. This choice will be active only if the clipboard has data thathave been entered using the Cut or Copy command within QGeNIe. You cannot Cut or Copy items
from other programs into QGeNIe Graph View. You can Cut or Copy nodes between two runninginstances of QGeNIe.

Insert Here submenu

The Insert Here submenu contains a list of all elements that can be drawn in the Graph View. Selectany of the items on the list to place that item at the current cursor position. See Components of
QGeNIe models section for more information on each item.

Select submenu

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Selection of items enables certain operations to be performed on them without affecting other items
that are not selected. Some options in QGeNIe will not be enabled unless some item has been selected.

The Select submenu contains the following options: All (select all items in the Graph View), Nodes(select all nodes in the Graph View), Submodels (select all submodels in the Graph View), and Arcs
(select all arcs in the Graph View).

Selection modifiers
Simple selection, such as rectangular selection and the selections by color, by type and by the
diagnostic type can be modified by pressing the SHIFT key or CTRL and SHIFT keys simultaneously.
Effectively, three selection modes are available:· Default selection: When neither SHIFT nor CTRL keys are pressed, leads to selection of only objects

selected. The remainder of the model elements becomes unselected.
· Add-to election: When the SHIFT key is pressed but CTRL key is not pressed, the objects selected by

the selection operation are selected, the remaining model elements do not change their selection
status.
· Invert selection: When both the SHIFT and the CTRL keys are pressed, the objects selected by theselection operation invert their selection status, the remaining model elements do not change theirselection status.

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For example, consider the Project model. Let us select all model elements (nodes and arcs) with
Ctrl+A first, then go to the Edit Menu and Select submenu. When keeping both SHIFT and CTRL
pressed, we select Arcs. The selection status for arcs will invert and only nodes will remain selected.
View nodes as submenu

The View nodes as submenu is used to select how the nodes should be displayed in the Graph View. It
is similar to the View As submenu in the Node menu but it applies to all nodes rather than to theselected nodes.

5.2.4

Tree view
QGeNIe provides an alternative method of model navigation known as Tree View. The Tree View in
QGeNIe is very similar to Windows tree view. It shows a hierarchical, alphabetically sorted list of all
networks currently open, and all the nodes in the network. Most operations available in the Graph
View can be also performed in the Tree View. Whatever changes are made in the Tree View, they arereflected immediately in the Graph View. The Tree View can be also used to navigate in the Graph
View, for example to open submodel windows. Another important feature of the Tree View is that you
can drag and drop nodes between different submodels and networks. We will illustrate the basic
elements of Tree View functionality in this section.
Tree View can be displayed or hidden by checking or un-checking the Tree option in the View Menu.
You can also use the keyboard shortcut CTRL+T to toggle Tree View display.

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Building blocks of QGeNIeThe Tree View panel can be detached from its position and placed anywhere on the screen by dragging
it using its title bar. It snaps back into place if dragged close to the left or right border of the QGeNIe
window.
Shown below is a typical Tree View panel.

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Building blocks of QGeNIeThe Tree View above shows three networks, Project, Productivity and Fever. A network or a submodel
can be expanded or collapsed by clicking on or beside its name. Fever is not expanded (hence, isdisplayed beside it). Project and Productivity are fully expanded (hence, is displayed beside them).
Nodes that are part of the networks are listed within each of the trees.
Note: Double clicking on the name of a network or submodel will also expand or collapse it.

Working with models, submodels, and nodes in the Tree View
Right clicking on the network name, node name, or submodel name will open the corresponding
Network Pop-up menu, Node Pop-up menu or Submodel Pop-up menu. You can use these menus tochange properties of the network, node, or submodel. Follow the links to each of the menus for more
information on how to performs these operations.

Moving nodes between networks and submodels
You can select any number of nodes and submodels in the Tree View and drag and drop them into any
model or submodel in the Tree View or the Graph View. You can perform the drag and drop
operations between different networks.

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Note: If the nodes are being dropped in a submodel which is part of the same network, then thenodes are moved to their new location.
If the nodes are being dropped in a different network or a submodel in a different network, then thenodes are copied to their new location.

Network Pop-up menu in Tree View
The Network Pop-up menu in the Tree View can be invoked by right clicking on the network name in
the Tree View.

Most of the choices are the same as in the Network Pop-up menu in the Graph View.
Open Main Model opens the main network in the Graph View.
Locate Focus Node finds the node designated as focus in the Graph View.
Network Properties opens the Network Properties sheet.
Rename allows for changing the name of the network interactively.
Clear All Evidence, dimmed if no evidence is present, allows for clearing all evidence.
Sort items causes the list of element names under the network to be sorted in alphabetical order.

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Close Network closes the network file. If any changes have been made to the network, then QGeNIe
will warn you that you may lose the changes. You can also close the network by clicking on the Close
button at the top right of the Graph View window or selecting Close option from the File Menu.

5.2.5

Status bar
The Status Bar is a horizontal bar located at the very bottom of the main QGeNIe window. The Status
Bar shows a short description of the command to be executed by the selected menu item or a tool on atoolbar on the left side and lists the number of model objects selected, the number of evidence nodes
and on the right-hand side the focus node designated in the network.

If there are any evidence nodes set in the network and any of the observed evidence propagates toother nodes, it will be indicated in the Status Bar as shown in the figure above. In the figure below, thetext on the Status Bar 1+1 evidence nodes indicates that there is one observation and one propagatedevidence (i.e., evidence implied by the observations). The focus of the model is variable Pregnancy.
The list of evidence nodes can be displayed by right-clicking on the text:

Any propagated evidence will be listed with a suffix [p]. To display only the observed evidence, rightclick on the status bar when holding CTRL key. To display only propagated evidence, right-click on thestatus bar when holding the SHIFT key. Clicking a node name on the list of evidence or target nodes
will locate the node in the Graph View.
Status Bar can be switched on and off by selecting or deselecting the Status Bar option from the View
Menu. A check mark appears next to the menu item when the Status Bar is displayed.

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5.2.6

Output window
QGeNIe includes a Output Window that is used for notifying the user about possible problems with themodel or program errors. The Output Window is usually shown in the bottom part of the screen, but
can be moved to any location by the user.

You can perform selections of the messages or clear the contents of the Output Window through acontext menu, available by right-clicking within the area of the window.

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Building blocks of QGeNIeThe Output window can be hidden or made visible by the user by changing the Output flag in the View
Menu (shortcut CTRL-U).

5.2.7

Help menu
The Help menu offers commands providing assistance to the user:

It contains four commands:
View Help (or F1 key) invokes QGeNIe on-line help, which is the document that you are reading at the
moment. QGeNIe on-line help is available in CHM, HTML and PDF formats. In addition to being
distributed with the program, it is also available on BayesFusion, LLC's support WWW pages. To exit
the on-line help, simply close its window.
QGeNIe Website will take you to the official QGeNIe website at https://www.bayesfusion.com/.

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Model Repository will take you to BayesFusion's interactive model repository through your default
web browser.
About QGeNIe shows the following simple window:

It displays a copyright notice, version number of your build of QGeNIe, license data, including license
ID, its expiration date, support expiration date, the license holder's name and institution.

5.3

Components of GeNIe models

5.3.1

Node types
QGeNIe supports three types of nodes:

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DeMorgan nodes, drawn as ovals and denoting uncertain variables representing DeMorgan gates.
This is the fundamental node type in QGeNIe is is created by default.

CAST nodes, drawn as ovals and denoting uncertain variables representing CAST gates. In order tocreate a CAST gate in QGeNIe, please hold the SHIFT key when clicking on the graph window whencreating a node.

Both node types nodes represent propositional variables, i.e., variables that can take only two values,
True and False. They are colored according to the marginal probability of the state True, unless this isreversed by a node setting, in which case the color shows the probability of the state False. The
difference between the two node types is in the model of interaction of parents used in theirdefinitions, DeMorgan and CAST.
Note: QGeNIe does not allow for using both DeMorgan nodes and CAST nodes in the same model.
CAST are legacy nodes that have been implemented in QGeNIe for the sake of compatibility withexisting models. We recommend that all new models be based on DeMorgan gate.
Submodel nodes, drawn as rounded rectangles, denote submodels, i.e., conceptually related groupsof variables. Submodel nodes are essentially holders for groups of nodes and helping with making
models manageable.

To learn how to create nodes and arcs between them, see the introductory section on Building a
QGeNIe model.

5.3.2

The DeMorgan gate
This section gives a brief introduction to the DeMorgan gate (Maaskant 2006, Maaskant & Druzdzel
2008, Druzdzel 2009), which is the fundamental model of interaction among propositional variables
in QGeNIe.
Those readers who were exposed to some logic in high school or college know that any logical function
can be expressed in one of the Augustus De Morgan's canonical forms, an alternative (OR) of
conjunctions (AND) or a conjunction of alternatives. It is also a fact stemming from so called De
Morgan laws that the OR function and a negation can express the AND function. A combination of OR
functions and negations can express any logical function. QGeNIe's DeMorgan gate offers essentially

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an intuitive way of expressing any logical function and, in particular, a combination of ORs, ANDs, andnegation.

Four basic influence types
The DeMorgan gate allows for modeling four basic types of influences that one variable (parent in the
directed graph) can have on another (a child in the directed graph): (1) a positive influence (a Cause)and (2) a negative influence (a Barrier), both combining with other causes using a noisy ORinteraction, (3) a required condition (a Requirement) and (4) a condition that prevents the effect fromhappening (an Inhibitor), both combining with other causes through an AND interaction.
We will now explain the meaning of the four types of causal influences and how they form any logical
function when combined.

Cause
A cause is a parent that has a positive influence on the child. Please note that this influence does notneed to be perfect. For example, smoking is generally believed to be a causal factor in lung cancer. Yet,
incidence of lung cancer among smokers, while much larger than incidence of lung cancer among nonsmokers, is still within a few percent. Hence, the conditional probability of lung cancer given that aperson is a smoker is still fairly low. The cause increases the probability of the effect but does not need
to be perfect in its ability to cause it.

Barrier
A barrier is a parent that decreases the probability of a child. For example, regular exercise decreases
the probability of heart disease. While it is a well established factor with a negative influence on heartdisease, it is unable by itself to prevent heart disease. One way of looking at a barrier is that it is dual toa cause: Absence of the barrier event is a causal factor for the child. One might go around the very
existence of barriers by using negated versions of the variables that represent them. In the example
above, one might define a variable Lack of regular exercise, which would behave as a cause of thevariable Heart disease. This, however, might become cumbersome if Regular exercise participated inother interactions in a model. It might happen that it is a parent of both Heart disease and Good
physical shape. Because Regular exercise decreases the probability of one and increases theprobability of the other, Barrier, which is a negated Cause, is a useful modeling construct.

Requirement
A requirement is a parent that is required for the child to be present. There are perfect requirements,
such as being a biological female is a requirement for being pregnant but there are also requirementsthat are in practice not completely necessary. For example, a sexual intercourse is generally believed to
be a requirement for pregnancy, but it is not a strict requirement, as pregnancy may be also caused byartificial insemination.

Inhibitor
An inhibitor is a parent that prevents the child from happening. For example, rain may inhibit wild
land fire. Like in the other types of interactions, the parent may be imperfect in inhibiting theoccurrence of the child. Fire may start even if there is rain. Similarly to the relationship between causes
and barriers, inhibitors are dual to requirements: Absence of an inhibitor event is a requirement forthe child. One might go around the very existence of inhibitors by using negated versions of the

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behave as a requirement for the variable Wild land fire. This, however, might become cumbersome if
Rain participated in other interactions in a model. It might happen that it is a parent of both Wild land
fire and Good crop. Because Rain is an inhibitor for the former and a requirement for the latter,
Inhibitor, which is a negated Requirement, is a useful modeling construct.

For those readers who like a concise mathematical formulation, the four types of causes interact with
their effect through the following logical formula:e = (c1 Ú c2 Ú Øb1 Ú Øb2) Ù r1 Ù r2 Ù Øi1 Ù Øi2 ,where:• cis stand for Causes• bis stand for Barriers• ris stand for Requirements• iis stand for Inhibitors
For the effect e to happen, all ris need to be present (one of them absent can bring down the effect) andall iis have to be absent (one of them present can bring down the effect). Any ci or bi can cause y but bi
work through their absent state, i.e., their absence can affect e. bi are just negated cis and iis are justnegated ris.
The DeMorgan gate is a canonical interaction model, similar to the Noisy-OR and Noisy-AND gates. In
order for the DeMorgan model to be applicable in practice, the following conditions have to be fulfilled(see also Diez & Druzdzel 2006).

· There should be a causal mechanism for each parent such that the parent is able toimpact the child variable in the absence of the other anomalies. If co-occurrence of
two or more parents is necessary to impact the child, the model may not besuitable. Please note that the impact is different between Causes/Barriers andRequirements/Inhibitors but each of them is capable of impacting the childvariable in separation of other parent variables.
· Are the causal mechanisms independent? This is quite likely the hardest condition

to verify, because our knowledge of the domain may not be precise enough to verify
that the causal mechanisms in question do not interact with one another. In
practice, however, unless there are known interactions, it is not unreasonable toassume that independence of causal influences (also known as the ICI condition)holds and the model can be applied. Should this condition not hold, it is typically

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possible to restructure the model in such a way that the ICI condition isapproximately satisfied.
· In case of existence of other, unmodeled causes (a non-zero Leak parameter), arethese unmodeled causes independent of all parents of the effect variable? Typically,
we assume that this condition holds unless there is evidence against it. In statisticalmodeling, this condition is often referred to as "independence of error terms."
We should keep in mind that the above conditions have to be satisfied approximately, as the modeling
effort involving QGeNIe graphs is an attempt to approximate reality and the methodology is robust to
minor violations of assumptions.

Parameter Elicitation
Of essence to model builders are the questions that are asked of an expert when eliciting the parameterfor each link type. These questions have to be clear so as to obtain reliable parametrization.
It is important to realize that the DeMorgan model is an Independence of Causal Influences (ICI)
model. A prominent member of the ICI family is the Noisy-OR model (Pearl 1988, Henrion 1989). Thismeans in practice that the parents influence the child independently of each other. Influence of eachparent can be specified in separation from the influences of the remaining parents. When specifying
the influence of a single parent on the child, it is assumed that each remaining parent is in its"distinguished state." The distinguished state is the state in which the parent has no effect on the child.
Because the effects of the four types of parents are different, their distinguished states are also
different. In our experience, this leads to considerable confusion among QGeNIe users, so we would
like to discuss this on an example and caution the readers to be very careful in selecting thedistinguished state of any node during parameter elicitation.

Cause
The distinguished state of a cause is the state in which the cause has no effect on the child. In QGeNIe,
it is always the state False. For example, Not being a smoker has no effect on Lung cancer in theexample introduced above. Not being a smoker is the distinguished state in this interaction. So is
Having no cancer.

Barrier
The distinguished state of a barrier is also the state in which the cause has no effect on the child. In
QGeNIe, it is always the state True. For example, regular Exercise may be thought as not influencing
the risk of Heart disease, and so it is the distinguished state in this interaction. So is No heart diseasein the child node.

Requirement
The distinguished state of a requirement is the state that is necessary for the effect to take place at all.
For example, Being a female is a requirement for becoming pregnant and it is the distinguished statein this interaction. In QGeNIe, it is always the state True. No pregnancy is the distinguished state of
the child node.

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Inhibitor
The distinguished state of an inhibitor is the state that has no effect on the child, i.e., the inhibiting
factor being absent. In QGeNIe, it is always the state False. For example, Rain is an inhibitor of Wild
land fire. Its distinguished state is No rain, which does not influence the wild land fire.

Now, for each type of interaction, the parameter pi associated with a link from a Cause or a Barrier icorresponds to the probability of the effect e happening if all parents are in their distinguished states(i.e., not acting upon e) and node i is not in its distinguished state. The parameter pi associated with alink from a Requirement or an Inhibitor i corresponds to the probability of the effect e not happening
if all parents are in their distinguished states (i.e., not acting upon e) and node i is not in its
distinguished state. There is also an additional parameter, called Leak which expresses the probabilityof the effect given that all parents are in their distinguished states. Rather than stating the questions to
the expert in the abstract, we explain them based on a simple example model below.

Example 1
Consider the following network based on the DeMorgan gate with two causes (Pneumonia and Flu),
one barrier (Bodily Temperature Balance), one requirement (Being Alive), and two inhibitors(Aspirine Intake and Tylenol Intake):

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We list questions for each of the causes in the above model with references to the states of all causes
underlined for more clarity.

The "leak" parameter
In order to elicit the leak parameter (0.01 in the above figure), the knowledge engineer has to ask thefollowing question: What is the probability of fever if an alive patient with bodily temperaturebalance, has neither pneumonia nor flu, and has taken neither aspirin nor Tylenol? QGeNIe helps in
the elicitation process by dimming the entire model except for the nodes involved and marking thenodes with the states that should be stated in the question.

Please note that all causes in the screen shot below are instantiated to F, the barrier is instantiated toT, requirement to T, and inhibitors to F. The conditions in the question include reference to the
distinguished states of each of the causes of Fever. The leak expresses the probability that fever
happens due to other, unmodeled causes.
We list questions for each of the causes in the above model with references to the states of all causes
underlined for more clarity.

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Questions asking for the causal strength of a cause C of the node Fever are framed in such a way that
only C is present and all other variables are in their distinguished states.
In order to elicit the strength of the influence of the node Pneumonia on Fever (0.9 in the figure), theknowledge engineer has to ask the following question: What is the probability of fever if an alive
patient with bodily temperature balance, has pneumonia but no flu, and has taken neither aspirin
nor Tylenol and no other unmodeled causal factors that may influence fever are present? QGeNIe
helps in the elicitation process by dimming the entire model except for the nodes involved and marking
the nodes with the states that should be stated in the question.

In order to elicit the strength of the influence of the node Flu on Fever (0.6 in the figure), theknowledge engineer has to ask the following question: What is the probability of fever if an alive
patient with bodily temperature balance, has flu but no pneumonia, and has taken neither aspirin
nor Tylenol and no other unmodeled causal factors that may influence fever are present? QGeNIe
helps in the elicitation process by dimming the entire model except for the nodes involved and marking
the nodes with the states that should be stated in the question.

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Barrier
Questions asking for the causal strength of a barrier B of the node Fever is framed in such as way that
only B is absent (please note that the distinguished state of a barrier is its presence, which does not
impact the effect) and all other causes and barriers are in their distinguished states.
In order to elicit the strength of the influence of the node Bodily Temperature Balance on Fever (0.6
in the figure), the knowledge engineer has to ask the following question: What is the probability offever if an alive patient with abnormal temperature balance, who has neither flu nor pneumonia,
and has taken neither aspirin nor Tylenol and no other unmodeled causal factors that may influence
fever are present? QGeNIe helps in the elicitation process by dimming the entire model except for thenodes involved and marking the nodes with the states that should be stated in the question.

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Requirement
Questions asking for the causal strength of a requirement B of the node Fever is framed in such as waythat only B is absent (please note that the distinguished state of a barrier is its presence, which does
not impact the effect) and all other causes and barriers are in their distinguished states.
In order to elicit the strength of the influence of the node Being Alive on Fever (0.98 in the figure please note that we allow for a patient to be just dead, with the body temperature still being high), theknowledge engineer has to ask the following question: What is the probability of no fever in a deadpatient with abnormal temperature balance, who has both flu and pneumonia, and has taken neither
aspirin nor Tylenol and no other unmodeled causal factors that may influence fever are present?
Please note that in this case all causes and barriers have to be in active (rather than distinguished)state and the question is asking for the probability of no fever! This is just the effect that requirementsinteract with other causes through the AND function. QGeNIe helps in the elicitation process bydimming the entire model except for the nodes involved and marking the nodes with the states thatshould be stated in the question.

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Inhibitors
In order to elicit the strength of the influence of the node Aspirine Intake on Fever (0.6 in the figure),
the knowledge engineer has to ask the following question: What is the probability of no fever if analive patient with abnormal temperature balance, who has both flu and pneumonia, and has taken
aspirin but not Tylenol and no other unmodeled causal factors that may influence fever are present?
QGeNIe helps in the elicitation process by dimming the entire model except for the nodes involved andmarking the nodes with the states that should be stated in the question.

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In order to elicit the strength of the influence of the node Tylenol Intake on Fever (0.9 in the figure),
the knowledge engineer has to ask the following question: What is the probability of no fever if analive patient with abnormal temperature balance, who has both flu and pneumonia, and has taken
Tylenol but not aspirin and no other unmodeled causal factors that may influence fever are present?
QGeNIe helps in the elicitation process by dimming the entire model except for the nodes involved andmarking the nodes with the states that should be stated in the question.

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Please note that in both cases all causes and barriers have to be in active (rather than distinguished)state and the question is asking for the probability of no fever! This is just the effect that inhibitors
interact with other causes through the AND function.
Each of the above questions can be adjusted to the needs of particular context, i.e., things can berephrased or omitted if they do not make sense.

Example 2
Consider the following network based on the DeMorgan gate with one cause (Fire Spreads Quickly),
one barrier (Fire Escapes Are Accessible), one requirement (People Are Still In the Building), and one
inhibitor (Fire Is Quickly Controlled):

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Building blocks of QGeNIeThe "leak" parameter
In order to elicit the leak parameter (0.05 in the above figure), the knowledge engineer has to ask thefollowing question: What is the probability of casualties if the fire does not spread quickly, fireescapes are accessible, people are still in the building, and fire is not quickly controlled? Please notethat the conditions in the question include reference to the distinguished states of each of the causes of
There Are Casualties. The leak expresses the probability that casualties happen due to other,
unmodeled causes. QGeNIe helps in the elicitation process by dimming the entire model except for thenodes involved and marking the nodes with the states that should be stated in the question.

Cause
Question asking for the causal strength of a cause C of the node There Are Casualties is framed in suchas way that only C is present and all other variables are in their distinguished state.

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In order to elicit the strength of the influence of the node Fire Spreads Quickly on There Are
Casualties (0.33 in the figure), the knowledge engineer has to ask the following question: What is theprobability of casualties if the fire spreads quickly, fire escapes are accessible, people are still in thebuilding, fire is not quickly controlled, and no other unmodeled causal factors are present? QGeNIe
helps in the elicitation process by dimming the entire model except for the nodes involved and marking
the nodes with the states that should be stated in the question.

Barrier
In order to elicit the strength of the influence of the node Fire Escapes Are Accessible on There Are
Casualties (0.66 in the figure), the knowledge engineer has to ask the following question: What is theprobability of casualties if the fire does not spread quickly, fire escapes are not accessible, people arestill in the building, fire is not quickly controlled, and no other unmodeled causal factors are present?
QGeNIe helps in the elicitation process by dimming the entire model except for the nodes involved andmarking the nodes with the states that should be stated in the question.

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Requirement
In order to elicit the strength of the influence of the node People Are Still In the Building on There Are
Casualties (0.88 in the figure), the knowledge engineer has to ask the following question: What is theprobability of no casualties if the fire spreads quickly, fire escapes are not accessible, there are nopeople in the building, fire is not quickly controlled, and no other unmodeled causal factors arepresent? Please note that in this case all causes and barriers have to be in active (rather thandistinguished) state and the question is asking for the probability of no casualties! This is just theeffect that requirements interact with other causes through the AND function. QGeNIe helps in theelicitation process by dimming the entire model except for the nodes involved and marking the nodes
with the states that should be stated in the question.

Inhibitor
In order to elicit the strength of the influence of the node Fire Is Quickly Controlled on There Are
Casualties (0.75 in the figure), the knowledge engineer has to ask the following question: What is theprobability of no casualties if the fire spreads quickly, fire escapes are not accessible, there arepeople in the building, fire is quickly controlled, and no other unmodeled causal factors are present?
Please note that in this case all causes and barriers have to be in active (rather than distinguished)state and the question is asking for the probability of no casualties! This is just the effect that
requirements interact with other causes through the AND function. QGeNIe helps in the elicitation
process by dimming the entire model except for the nodes involved and marking the nodes with thestates that should be stated in the question.

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Each of the above questions can be adjusted to the needs of particular context, i.e., things can berephrased or omitted if they do not make sense.

5.3.3

The CAST gate
In addition to the DeMorgan gate, QGeNIe implements another interaction model: the CAST gate
(Chang 1994). CAST stands for CAusal STrengths logic. We have attempted to find a probabilistic
interpretation of the CAST gate and we think that it is challenging with non-obvious meaning of its
parameters. Still, CAST gate has been used widely, especially in military applications, through two
programs popular in 1990s, SIAM and Causeway, both apparently no longer available. Whileinteresting and useful in practice, we believe that in the context of probabilistic models, the CAST gate
is semantically less clear than the DeMorgan gate and we found that it sometimes causes problems
with knowledge elicitation from experts, especially around CAST's baseline probability (discussed
below). We have implemented it in QGeNIe for the sake of completeness and compatibility withexisting models and software, although we do not allow for combining DeMorgan and CAST gates inthe same model, so the user has to choose whether he/she is modeling using DeMorgan gates of the
CAST gates.
We focus in this section on the CAST gate, providing definitions and descriptions of the CAST
parameters. Because this section is necessarily brief, we refer interested readers to the paper by Chang(1994).

Baseline probability
Similarly to the DeMorgan gate, every CAST node is characterized by a parameter called baseline
probability. For nodes without parents, this parameter has a fairly clear probabilistic interpretationand is simply the prior probability of the proposition represented by the node.
However, when the node has parents, the baseline probability can no longer be interpreted as the priorprobability of the proposition. It cannot be given the interpretation of a leak parameter. Only jointlywith a specification of the influence of parents, causal strengths (described in the next paragraph), it ispossible to derive conditional probability distributions over the node, something that a probabilistic
model needs.

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It would be fair to state that the baseline probability P(X) is the probability of a proposition X when it
is isolated from influences of any other modeled variables, so it is close to the marginal probability of
X.

Causal strengths
Every arc connecting a pair of CAST nodes is described by two parameters, which are called causal
strengths. Causal strengths take values from the interval [-1..1]. They are not probabilities but rather
an expression of fractional change in the baseline probability of the child node as an effect of theparent node being True or False respectively. Because they can take both positive and negative values,
they are convenient in describing positive or negative character of influences.
The following figure from (Chang 1994, Figure 2.3) illustrates the meaning of causal strengths
associated with an influence of a variable Y on a variable X:

Causal strength C(X|Y) expresses the percentage of change of the baseline probability toward theextremes (0 and 1). When positive, its value tells us the percentage of change from the current
probability p toward the probability 1.0. When negative, it expresses the percentage of change from thecurrent probability p toward the probability 0.0. The top part of the figure shows what happens when
the baseline P(X)=0.2 and C(X|Y)=0.4. In this case, the probability of X changes from its original valueof 0.2 to 0.2+(1.0-0.2)*0.4=0.52. The middle part of the figure shows what happens when the baseline
P(X)=0.5. In this case, the probability of X changes from its original value of 0.5 to 0.5+(1.0-0.5)*0.4=0.7. Finally, the bottom part of the figure shows what happens when the baseline P(X)=0.5 gets
affected by a negative causal strength C(X|Y)=-0.4. In this case, the probability of X changes from its
original value of 0.5 to 0.5-(0.5-0.0)*0.4=0.3.

Aggregation of causal strengths
Causal strengths, similarly to parameters in the DeMorgan gate, describe individual influences thatneighboring nodes have on each other. This allows for a concise description of how a node is effectedby its parents. When multiple parents are present, the CAST logic determines how their influences are
combined with each other. Combining positive influences is quite straightforward and so is combining
negative influences. Calculation of a mixture of positive and negative influences is more problematic,
although resting on reasonable heuristic assumptions.

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5.3.4

SubmodelsQGeNIe allows for placing groups of nodes into submodels. Submodels are special types of nodes thathost sub-graphs of the entire graph and make the Graph View structured hierarchically. Submodelingfacilitates modularity in large models. We advise to make a generous use of submodels in case yourmodels become larger than, say 50 variables. A large number of variables may not fit on the screen andmay make the interaction with the model cumbersome. The internals of a submodel, along with its
structure can be examined in separation from the entire model.

Creation of submodels, moving nodes, navigating throughsubmodelsTo create a submodel in QGeNIe, select Submodel from the Tool Menu or the Submodel (the Standard Toolbar and click on the Graph View. You will see a new submodel.

) tool from

Submodel windows can be opened by double-clicking on the Submodel icon or right-clicking on thesubmodel icon and choosing Open Submodel from the Submodel properties menu.
Nodes can be moved between submodels by selecting them in the source submodel, dragging, anddropping them in the destination submodel. For example, we might want to create a submodel for thevariables Increased Load and Higher Speed Requirements in the following model. We do this bycreating a submodel node, renaming it to Customer Wishes, and then dragging and dropping the nodes
Increased Load and Higher Speed Requirements to the new submodel.

The resulting model will look as follows:

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Submodels are opened by double-clicking on them. Double-clicking on the submodel Customer Wishes
yields the following:

One thing that becomes less clear in submodels is the connections that a submodel has with theexternal world. QGeNIe does not normally show arcs that are coming from outside or that go to theoutside world. It does let the user know that there are such connections. First of all, by showing theseconnections as coming into the submodel node (note the arcs from the submodel node Customer
Wishes coming into the node Technical Problems at the main model level). It also adds small triangleshaped marks on the left and right sides of the internal submodel nodes showing that there areincoming and outgoing arcs respectively. The user can examine these connections by placing the cursor
over the small triangle. This will display the name of the child of the node in another submodel asfollows:

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You can locate the child of this node by right clicking and choosing Locate Child from the Node Pop-up
menu:

Alternatively, right-clicking on the small triangle on the right-hand-side shows a pop-up menu with alist of children of the node in other submodels. Both actions lead to finding the node and flashing it on
the screen as shown below:

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It is possible to add arcs between nodes that are located in different submodels in the very same waythat arcs are added between nodes in the same submodel. When more than one arc is drawn between asubmodel and a node, then QGeNIe draws a double arrow arc from the submodel to the node as shown
above. All the above functions can be also performed through QGeNIe Tree View.

Submodel properties
Submodel properties sheet can be displayed by right clicking on the name of the submodel in the Tree
View or right clicking on the submodel icon in the Graph View. This will display the Submodel Pop-up
menu. Select Submodel Properties from the menu.

Note : Double clicking on the submodel will open the graph view of the submodel, it will not open the
Submodel properties sheet.
The Submodel properties sheet consist of two tabs: General and Format. The General tab allows tochange the identifier and the name of the submodel, the Format sheet allows to change the graphical

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properties of the submodel icon and is identical to the property sheet described in node property
sheets.

General tab
The General tab displays the Identifier and the Name of the submodel, along with the submodel's
basic statistics.

Identifier displays the identifier for the submodel, which can be modified by the user. Identifiers must
start with a letter, and can contain letters, digits, and underscore (_) characters. The identifier for the
network shown above is Customer_Wishes.
Name displays the name for the submodel, which is specified by the user when the submodel is firstcreated. There are no limitations on the characters that can be part of the name. The name for the
network shown above is Customer Wishes.
The Objects in the submodel lists counts of various types of objects and numerical parameters in thesubmodel. They give an idea of the submodel's complexity.

Format tab

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Building blocks of QGeNIeThe Format tab allows to modify the visual properties of the submodel icon, i.e., how the submodelicon is displayed in the Graph View.

The Format tab allows for specifying the physical appearance of the submodel node in the Graph
View. The user can specify the font, its size, border width, the color of the text, border, and interior of
the submodel node.

Other submodel operations
Submodel Popup menu is slightly different for the Graph View and the Tree View.

Submodel Pop-up menu for the Graph View
The Submodel Pop-up menu for the Graph View can be displayed by right clicking on the submodelicon in the Graph View.

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Building blocks of QGeNIeOpen Submodel opens the submodel in a new Graph View window.
Submodel Properties opens the Submodel Properties sheet.
Propagate Color to Parent allows for passing the color of the node (this includes a submodel) icon to
the parent submodel icon. This is useful when we want to summarize the effect of a submodel in one
node to which we pass the color of a selected outcome node from inside the submodel.
Locate Color Source is an operation opposite to Propagate Color to Parent and it allows to find thenode that is passing its color to the current submodel icon.
Select Color Source is an operation symmetric Propagate Color to Parent and it allows to find thenode that will pass its color to the current submodel icon. When selected, the following dialog pops up.

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Building blocks of QGeNIeThe list of nodes in the dialog contains all nodes inside the submodel. The can besearched using wildcard characters of * and ?.
Rename allows you to rename the submodel by placing the submodel icon in edit mode. You can alsorename a submodel by modifying the Name field in Submodel Properties sheet.
Delete deletes the selected submodel.
Resize to Fit Text resizes the submodel icon so that it fits the entire submodel name.
Annotation... opens up the annotation dialog so that you can add an annotation to the submodel (seeAnnotations section for more information).

Layout submenu

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Most of the commands on the Layout submenu are the same as those in the Layout Menu. The only
commands here that are not found in the Layout Menu are:Make Same Size (enabled only if two or more items are selected in the Graph View) resizes theselected items so that they are the same size as the item that was right clicked.
Make Same Width (enabled only if two or more items are selected in the Graph View) resizes theselected items so that they are the same width as the item that was right clicked.
Make Same Height (enabled only if two or more items are selected in the Graph View) resizes theselected items so that they are the same height as the item that was right clicked.
Submodel Popup menu for the Tree View:

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Building blocks of QGeNIeThe Submodel pop-up menu for the Tree View can be displayed by right clicking on the submodel
name.

The two commands that are not available in the Graph View are:
Locate locates the submodel in the Graph View of its parent model or submodel. Once located, thesubmodel icon flashes several times on the screen to attract user attention.
Sort Items sorts the list of nodes or submodels of the current submodel listed in the tree view inalphabetical order.

5.3.5

Arcs
An arc in a Bayesian network denotes an influence, i.e., the fact that the node at the tail of the arc
influences the value (or the probability distribution over the possible values) of the node at the head of
the arc.
One remark about editing models is that QGeNIe does not allow moving arcs between nodes, i.e., it is
not possible to select and drag the head or the tail of an arc from one node to another. If this is what
you want, the way to accomplish this task is to first delete the original arc and then create a new arc.
QGeNIe displays also arcs between nodes and submodels. An arc from a node N to a sub-model Smeans that at least one node in S depends on N. An arc from a sub-model S to a node N means that N
depends on at least one node in S. An arc from a sub-model S1 to a sub-model S2 means that there is atleast one node in S2 that depends on at least one node in S1. Arcs between sub-models can be doubleheaded, in which case the relations listed above is reciprocal. For example, a double-headed arrow
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the outside of the current sub-model window. Existence of arcs coming from outside of the current
sub-model and ending in a node in the current sub-model is marked by a small triangle on the lefthand side of the node. Existence of arcs originating in a node in the current sub-model and ending in ahigher-level sub-models is marked by a small triangle on the right-hand side of the node. These links
can be followed by right-clicking on the small triangles.
Cycles in the graph, i.e., directed paths that start and end at the same point, are forbidden (unless thegraph is dynamic, such as a Dynamic Bayesian Network, which is covered in a separate section).
QGeNIe will not allow you to draw cyclic graphs. Please note that even though QGeNIe will enforce
that the underlying graph is acyclic, you may still be able to observe cyclic graphs involving submodel
nodes. This is due to the meaning assigned to arcs between submodels.

5.3.6

Node status icons
Each node in the Graph View is marked by one or more node status icons. These are tiny icons
displayed in the lower right corner of the node icon. There are four different node status icons:Observed, Implied, Controlled and Focus. We will explain their meaning on simple examples.

Observed Status Icon
The Observed status icon (e.g., ) is displayed bold when the user enters evidence into a node and it
signifies that the node is an evidence node. Nodes Hot Weather and Climate Control in the Building in
the following model are evidence nodes and are marked with the Observed status icons:

Implied Status Icon
Sometimes, observing a node implies the values of other nodes. For example, observing that a patientis male in a medical decision support system will imply that he is not pregnant. This is possible
because the program realizes that for a male patient pregnancy is impossible. QGeNIe marks the nodes
whose values are implied by observations of other nodes by the Implied status icons. The Implied
status icon is identical to the Observed status icon, but have yellow background (e.g., ). In thefollowing example, the Sex variable has been observed to be False and the value of the Pregnancy
variable has been determined by GeNIe to be False.

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Building blocks of QGeNIeControlled Status Icon
Controlling the value of a node is different from observing it (see Changes in structure section). Whena node is controlled, QGeNIe marks it with the Controlled status icon ( ). Node Penalties in the model
below has been manipulated and marked as Controlled.

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Focus Status Icon
The Focus status icon ( ) has to do with QGeNIe's support for calculations of the value of informationand value of manipulation. Node Negative Profit in the above model has been designated as Focus.

5.3.7

Text boxes
You can put an arbitrary text in the Graph View window. This text may be useful as a comment
explaining the details of the model. To add a text you need to create a Text box.
Select the Text box ( ) button from the toolbar or Tools menu (note that the cursor shape changes).
The Text box button will become recessed. Move the mouse to a clear portion of the Graph View andclick the left mouse button. You will see a rectangle appear on the screen:

You can type any text inside the box. You can use any of the regular editing tools, such as cursor
movement, selection, Cut, Copy, and Paste. After you have typed your text, press Enter or click
anywhere outside of the box. The box may look as follows:

You can change the font type, style, size, and color using appropriate tools from the Format Toolbar.
Shown below are some effects of using the Format Toolbar:

You can always come back to editing the text in the box by double-clicking on the box. Double-clicking
makes the text visible for editing again.
You can select the box by single-clicking on it. A selected box shows its boundaries and two small
squares at its left and right boundary.

You can re-size a selected box by dragging on one of these small squares. You can delete it by pressing
the Delete key. You can drag the box to a new location by clicking on it and holding the mouse button
down while moving it to a new location.

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5.3.8

Annotations
QGeNIe supports annotations for nodes and states of nodes. Following the idea that one of the main
goals of a model is documenting the decision making process, annotations are useful for explaining
function of nodes and states, or to note down just about anything the user feels is important regarding
the node or state.

Annotations for nodes
You can specify annotations for nodes by right clicking on the node and selecting Annotation from theNode Pop-up menu. This will display the annotation box as shown below:

Enter the annotation in the white blank space and click on OK to save it. Once an annotation has beensaved against a node, QGeNIe displays a small yellow note ( ) beside the status icon of the node. Toview the annotation for a node, hover the cursor over the note. QGeNIe will display the annotation as
follows:

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Double click on the note to display the annotation box, which will allow you to edit the annotation.
To delete an annotation, double click on the note and delete all contents of the annotation box.

Annotations for arcs
You can annotate arcs between nodes as well. To annotate an arc, right click on it and choose Annotatefrom the pop-up menu that shows:

Choosing Annotate brings up the annotation window. Hovering over the yellow stick-it-note shows the
text of the annotation.

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5.4

Model and component properties

5.4.1

Network properties
Network properties sheet summarizes all properties that are specified at the model level. It can be
invoked in three ways:
1. Double clicking on a clear area of the network in the graph view.
2. Right clicking on the name of the network in the Tree View or right clicking on a clear area of
the network in the Graph View. This will display the Network Pop-up menu. Select Network
Properties from the menu.
3. Select Network Properties from the Network Menu as shown below.

The Network properties sheet, once opened, consists of several tabs.

General tab

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Building blocks of QGeNIeThe General tab of Network properties (shown below) consists of the following fields:

Identifier displays the identifier for the network, which is user-specified. Identifiers must start with aletter, and can contain letters, digits, and underscore (_) characters. The identifier for the network
shown above is Productivity.
Name displays the name for the network, which is also user-specified. There are no limitations on thecharacters that can be part of the name. The name for the network shown above is also Productivity.
Description is a free text describing the network. Please note that a model is a documentation of the
problem and use descriptions generously.

Sliders tab
The Sliders tab of Network properties (shown below) allows for choosing the colors representing
various degrees of belief in the propositions represented by model variables.

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Building blocks of QGeNIeThe default colors, which we recommend, range from deep red to deep green, representing probability
zero and one respectively. Should you change them and later realize that you would prefer to return tothe original settings, you can always use the Restore defaults to return to thecolors defined by previous
pressing of the Make default button or Restore factory settings to return to the red-green scheme built
into the program. Color transitions can be selected through the Blending function pop-up menu:

User properties tab
User properties tab allows the user to define properties of the model that can be later retrieved by anapplication program using SMILE.

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For example, we can add a property AUTHOR with the value "http://www.bayesfusion.com/".
Neither QGeNIe nor SMILE use these properties and they provide only placeholders for them. They
are under full control and responsibility of the user and/or the application program using the model.
QGeNIe and SMILE support only editing and storing/retrieving them.
Button Add invokes the following dialog that allows for defining a new user property:

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Buttons Edit and Delete allow for editing and removing a selected property, respectively.

Summary tab
Summary tab contains summery statistics of the network, as illustrated below:

Statistics focus on the structural properties of the network, such as the number of nodes of each type in
the network, the average and the maximum in-degree (the number of parents of a node), the averageand the maximum number of outcomes of nodes, node counts by their diagnostic type, the number ofarcs and the number of text boxes, and, finally, the number of states and parameters. Independent
parameters take into account that some parameters are just complements, making sure thatprobabilities have to add up to 1.0. The Productivity model, shown in all illustrations in this section,
contains 4 nodes, all of which are DeMorgan nodes. The number of arcs (3) and the average in-degree(0.75) give an idea of the structural complexity of the network. Elapsed time of the last inference callgives an idea of the difficulty that your computer system experienced with solving the model.

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5.4.2

Submodel properties
The Submodel properties sheet can be displayed by right clicking on the name of the submodel in theTree View or right clicking on the submodel icon in the Graph View. This will display the SubmodelPop-up Menu. Select Submodel Properties from the menu.

Note : Double clicking on the submodel will open the graph view of the submodel, it will not open the
Submodel properties sheet.
The Submodel properties sheet consist of two tabs: General and Format.

General tab
The General tab displays the Identifier and the Name of the submodel, along with the submodel's
basic statistics.

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Identifier displays the identifier of the submodel, which is user-specified. Identifiers must start with aletter, and can contain letters, digits, and underscore (_) characters. The identifier for the network
shown above is Customer_Wishes.
Name displays the name for the submodel, which is user-specified. There are no limitations on thecharacters that can be part of the name. The name of the submodel shown above is Customer Wishes.
The Objects in the submodel lists counts of various types of objects and numerical parameters in thesubmodel. They give an idea of the submodel's complexity.

Format tab
The Format tab allows to modify the visual properties of the submodel icon, i.e., how the submodelicon is displayed in the Graph View.

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Building blocks of QGeNIeThe Format tab is similar in function to the Format tab of the Node properties sheet.

5.4.3

Tools menu and Standard toolbar
Tools menu is the main bag of tools for building models.

Most of these tools are replicated in the Standard Toolbar, which is a bar with buttons that offer quick
mouse shortcuts for a number of menu commands.

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It is also accessible in a floating form

Standard Toolbar can be made invisible using the toggle command Toolbar on the View Menu. It canbe also moved to any position within QGeNIe application window. To move the toolbar from a locked
position, click on the vertical bar at the left edge of the toolbar and drag it to its destination. Besides
the standard buttons for opening, closing, and saving a file, this toolbar has buttons for selecting
various tools found in the Tool Menu.
We review here Standard Toolbar tools that mimic the Tools menu tools and allow for creating objects
in the Graph View window. The drawing tool currently selected is marked by a dot on the left side ofits name in the Tools menu. Each of the Tool menu tools can be also selected using a correspondingStandard Toolbar icon. While choosing a tool from the Tools menu, selects the tool for a single editing
action (with the exception of the Select Objects tool, which is the default tool), the tools on theStandard Toolbar work also in sticky mode. When a drawing tool on the Standard Toolbar is doubleclicked, it remains selected until it is deselected (single-clicked upon) or another tool is explicitly
selected. The tool Node (), draws a node in the Graph View. Submodel (Text Box ( ) allows for creating on-screen comments, and Arc (between two nodes.
The BayesFusion internet resources button () draws a submodel,) allows for creating an arc) is a short-cut to the Internet Resources menu:

Clicking on any of the links leads to opening a corresponding web page in your default web browser:
Main BayesFusion Website:
The main page of BayesFusion's Internet web site.
Interactive Model Repository:
The site of BayesFusion's interactive model repository, powered by BayesBox.
Documentation:

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A page with links to various documents, manuals, etc.
Support Forum:
BayesFusion's support forum, a place where you can ask questions and browse previous questions
and our specialists' answers.
YouTube Channel:
BayesFusion's YouTube channel with movies and instructional videos.
Contact Us:
While we can be reached most reliably by EMail, this is a link to a web-based contact form that canbe used to send us an Email message.

5.4.4

Node properties
Node properties sheets allow for modifying properties of model nodes. They can be opened rightclicking on the name of the node in the Tree View or right clicking on the icon of the node in the Graph
View. This will display the Node Pop-up menu. Select Node Properties from the menu.

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The Node properties consist of two tabs: General tab and Value tab.

General tab
The following image shot shows a snapshot of the General tab:

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Building blocks of QGeNIeThe General tab contains the following properties:
Identifier displays the identifier for the node, which is user-specified. Identifiers must start with aletter, and can contain letters, digits, and underscore (_) characters. The identifier for the network
shown above is High_Productivity.
Name displays the name for the network, which is user-specified. There are no limitations on thecharacters that can be part of the name. The name for the network shown above is High Productivity.
Identifiers, which are meant to refer to nodes, may be too cryptic when working with a model, so we
advise that QGeNIe users rely only on Names. The reason for having both, the Identifier and the Nameis historical and has to do with the fact that nodes may be referred to in equations. In that case, thereference should avoid problems with parsing, which could easily appear with spaces or special
characters. QGeNIe allows for creating and editing identifiers because it is common that QGeNIemodels are exported to GeNIe, where identifiers become important.
Node Type (in the picture, it is Chance - DeMorgan) cannot be changed in the General tab and serves
only informational purpose. Node type is set during node creation.

Value tab

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Building blocks of QGeNIeThe Value tab shows the marginal probability distribution over the two states of a QGeNIe node: Trueand False.

The Piechart () button turns on and off the display of this probability distribution as a pie chart.

The Show QuickBars ( ) buttons turns on and off graphical bars in the result spreadsheet (a list of
states with their posterior probabilities).

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Transparent mode
By default, the node property sheets are displayed in opaque mode, i.e., when open, they coverwhatever is under them. Here is a screen shot of the Project model

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It is possible to make the property sheets transparent, which may be handy when navigating through amodel. Transparent mode allows to see what is under the property sheets. The same model intransparent mode looks as follows

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To toggle between the opaque and transparent mode, check the Transparent flag in the System Menu,
available by clicking on the icon in the upper-left corner of the node property sheets.

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Building blocks of QGeNIeThe setting hold only for the currently open property sheet and only as long as it is open.
The transparent mode may be especially useful when the property sheets are maximized, in which caseit allows to see what else is on the screen and in the Graph View window.

5.4.5

Arc properties
Arc properties can be controlled through the Arc Properties Menu, invoked by right clicking on the arc
in the Graph View.

Show Sliders allows for entering the strength of influence parameter, described in detail in the The
DeMorgan gate section.
Change Type submenu allows for changing the arc type. Arc types are described in detail in the The
DeMorgan gate section. This operation is useful when we realize that the influence type that we havedesignated when constructing the model is incorrect. Please do make sure to revisit the strength of
influence elicitation, as the meaning of the strength of influence parameter may have changed with thechange of the arc type.
Locate Parent and Locate Child allow for finding the parent and the child node respectively. This
operation is especially useful when the model is complex and the nodes are not clearly visible or in casethe parent or the child are located in a submodel.
Delete removes the arc permanently.
Annotation opens up an Annotation dialog and allows for adding an annotation to the arc.

5.5

Visual appearance, layout, and navigation

5.5.1

Introduction
One of the major strengths of QGeNIe is its graphical user interface. QGeNIe users have repeatedly
and consistently praised it for being pleasant, easy to use, and intuitive. It literally cuts the model
development time by orders of magnitude. Because this is the bottleneck in applying decision-theoretic
methodologies in practice, it translates directly to considerable savings. We have paid a lot of attention
to detail and one of the reasons why it is so good is its physical appearance. While each of the sections

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Building blocks of QGeNIeof this manual contains elements of graphical user interface, which we hope the reader will experienceas pleasant and intuitive, this section points out some ways in which your interaction with the program
can be enhanced and made more efficient.

5.5.2

Viewing nodes in the Graph View
We review in this section two important aspects of viewing nodes in the Graph View.

Icons and bar charts
There are two ways of viewing nodes in the Graph View: (1) as icons, and (2) as bar charts. The
following image shows the Productivity network from the Hello QGeNIe! section, in which every nodeis shown as an icon:

The following image shows the same network, in which every node is shown as a bar chart:

Bar charts display graphically the node's marginal probability distributions. The advantage of seeing anode as a bar chart is that we can see at any point in time its marginal probability distribution
expressed by bars and percentages. The disadvantage is that a bar chart takes more space on the screen
and may unnecessarily draw user's attention. We advise that those nodes, whose marginal probability

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distributions are of interest, are viewed as bar charts and others as icons. To switch between the twoview, select the nodes in question and use the Node Pop-up menu

To view all nodes as bar charts or as icons, perform this operation when no nodes are selected.

Names and identifiers
There is another important element of viewing nodes: Viewing their IDs or their names. IDs are shortand play the role of variable names. QGeNIe uses them for the purpose of compatibility with GeNIe,and GeNIe, in turn, uses them for the sake of compatibility with equation-based variables, wherereference to other nodes in a node's definition has to be through a unique identifier. Identifiers have tostart with a letter followed by any combination of letters, digits, and underscore characters. Names are
longer and have no limitations on the characters that they are composed of. A model viewed withidentifiers looks cryptic.

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A model viewed with names is more digestible to human users.

To switch between IDs and names, please use the View Menu

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We advise that, unless there are important reasons for viewing them as identifiers, nodes be viewed bynames. This is more readable for human users.

5.5.3

Zooming and full screen mode
Zoom ( ) is one of the navigational tools and allows for zooming in and out the Graph View byclicking with the left mouse button and the right mouse button respectively. An alternative way ofzooming is through rolling the mouse wheel while holding down the SHIFT key. Yet another ispressing CTRL-+ and CTRL--. The effective zoom percentage is displayed on right side of the Standard
Toolbar. Zooming can be also performed directly through the Zoom menu.

Zoom menu allows for choosing from a small set of predefined zoom percentage values. Two additional
useful functions are Fit to window and Fit selection allow for further customization of the display. Fit
to window selects the zoom value that makes the entire model visible in the Graph View. Fit selection

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will select an optimal zoom value to make the selected model elements centered and visible in theGraph View.
An additional functionality, full screen view is useful in case of limited screen space. To enter the fullscreen mode, press the Toggle full screen () button or choose Full Screen from the View Menu.

The Graph View will be expanded to cover the full screen (physically!).

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To exit the full screen mode, please press the Close full screen button in the following dialog that ispresent in the upper-right corner of the screen.

This will result in returning to the standard Graph View. It is also possible to remove this dialog
without exiting the full screen mode, for example in case the dialog covers important parts of thescreen. To close the dialog, click on the on the top-right corner of the window. Without the dialog
available, you can still exit the full screen mode by pressing the Esc of F11 keys.

5.5.4

Format toolbar and Layout menu
Note: All operations performed using the Format toolbar are applicable to the currently selected itemin the Graph View. All new items created follow the current settings on the Format toolbar. Tree View
cannot be changed using the Format Toolbar.

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Building blocks of QGeNIeThe Format toolbar includes tools for refining the aesthetic aspects of the Graph View. It can be madeinvisible using the toggle command Toolbar-Format on the View Menu. It can be also moved to any
position on the screen in its free floating form. To move the toolbar from a locked position, click on thevertical bar at the left edge of the toolbar and drag it to its destination. The Format toolbar has buttons
for changing font, color, and size of the text, and for alignment of text and various nodes in the model.
Here is the Format toolbar

In its free-floating form, the Format toolbar appears as follows

Font properties buttons

allow for selecting the font, its size, and appearance (Bold or Italic).
Text justification tools

allow for specification of the text alignment within text boxes, notes, and nodes.
Color tools

allow for setting the interior color of node and submodel icons, line color, and text color. This is similar
to color selection in the Format tab of Node Properties sheet.
Line width pop-up tool is used to select the width of the boundary lines of the node/submodel icons.

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Node/Submodel Align buttons

become active when at least two nodes are selected in the Graph View. They allow for aligning thedrawing of nodes to each other or to the grid. Their functionality is self-explanatory and essentiallysimilar to the functionality of most drawing software packages.
Show gridlines toggles display of the grid, i.e., a mesh of perpendicular horizontal and vertical lines inthe background of the Graph View. The grid is useful in drawing and aligning nodes.
Align to grid aligns the tops and left sides of selected objects to the nearest grid lines. At least one
object must be selected for this option to be active.
Align left and Align right align the leftmost and rightmost points of the selected objects, respectively.
Align top, Align bottom align the tops and bottoms of the selected objects, respectively.
Center horizontally and Center vertically align the object centers in horizontal and vertical direction
respectively.
Distribute horizontally and Distribute vertically distribute evenly the selected objects respectively
horizontally and vertically between the position of the farthest nodes. Both tools will be active only if at
least three objects are selected.
Bring to front brings the selected object to the front so that none of its parts is covered by otherobjects.
Send to back sends the selected object to the back so that none of its parts covers any other objects.
Finally, Resize to fit text changes the size of the selected nodes so that the text displayed in the nodes
shows in its entirety.

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Building blocks of QGeNIeThe functionality of Node/Submodel Align buttons is repeated in the Layout menu

There are two additional functions offered by the Layout menu that are not offered by the Format
toolbar:Snap to grid makes all new nodes that are created in the Graph View aligned to the grid. (If you want
to align existing nodes to the grid, select them and use the Align to Grid tool, described above.) When
Snap to grid is on, dragging of nodes, with the Snap to Grid option on, will not be smooth, as nodes
will jump from one grid line to another.
Grid Properties... opens up the Grid Properties dialog shown below

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Building blocks of QGeNIeShow grid checkbox has a same function as the Show Gridlines option in the Layout menu. Whenchecked, the grid lines are displayed in the Graph View.
Snap to grid checkbox has the same function as the Snap to Grid option in the Layout menu. Whenchecked, all new nodes created will be automatically aligned to the grid.
Spacing defines spacing (in pixels) between the lines of the grid. A smaller value will result in a finer
grid.
Style defines how the grid lines will be displayed:
Solid lines makes the grid lines solid.

Dotted lines makes the grid lines dotted.

Dots makes only the intersection points of the grid lines displayed.

Color sets the color of the grid lines. You can select any color from the palette or define your own color.
Grid color selection is similar to color selection for nodes. While darker colors are better when the gridstyle is dotted or dotted lines, we advise lighter colors for solid grid lines so that they do not clutter theGraph View unnecessarily.
Restore Defaults restores the grid display settings to the original factory settings.

5.5.5

Graph layout functions
While it is not very likely, it is possible that a model constructed in the Graph View becomes too
messy. For such cases, QGeNIe supplies functions that arrange nodes within the Graph View
automatically using two graph layout algorithms: Spring Embedder and Parent Ordering. Layout
algorithms are computationally complex and their output may be far from perfect from the point ofview of a human user. It is generally a good idea to treat their output as a starting point for manual
arrangement of nodes.

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Spring embedder
The Spring Embedder algorithm (Quinn & Breuer 1979; Eades 1984) is a more sophisticated algorithmof the two and it yields fewer arc crossings and a generally better layout of the graph. It rearranges the
positions of the nodes in such a way that the nodes do not overlap each other, arc crossings areminimized, and the layout is readable for the user.
Clicking on the Graph Layout/Spring Embedder opens the following dialog:

Ideal arc length specifies the ideal distance (in pixels) between two interconnected nodes. Please notethat not all arcs will be of this length, the algorithm may modify this length so as to reduce overlaps.

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Iterations specifies the number of iterations for the layout algorithm. Reducing this number, especiallyfor very large networks, will translate directly to shorter execution time.
Randomize positions at startup, if checked, causes node positions to be randomized before runningthe layout algorithm. If it is not checked, the algorithm receives the original node positions.

Parent ordering
Parent Ordering is a simple algorithm for graph layout that essentially places the elements of themodel in top-down or left-to right order starting with the parent-less ancestors and ending withchildless descendants. The Graph Layout/Parent Ordering option opens the following dialog:

Top to Bottom: This places the nodes in the model in the top to bottom on the graph view.
Left to Right: This places the nodes in the model from left to right on the graph view.
Spacing: This specifies the distance between nodes.

5.5.6

Selection of model elements
Often, when constructing models, we want to select their parts so as to change them as a group. The
simplest way of selecting a model element is by left-clicking on them. Another common way is byselecting an area in the Graph View using the mouse. Everything in the area selected by the mouse will
be selected. Adding new elements to the selected set can be accomplished by holding the SHIFT key
when left-clicking on the mouse.
Edit Menu offers several other selection shortcuts that will may make your life easier:

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Building blocks of QGeNIeAll, with a shortcut CTRL+A works in most views and selects all elements (for example, nodes,
submodels, and all arcs between them) of the current Graph View.
Select Evidence Nodes selects those nodes, for which the evidence has been set by the user. The
function is dimmed out if no evidence is present in the model.
Select Nodes... dialog allows for sophisticated selection of nodes based on their names.

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Check boxes next to variable names on the left-hand side allow us for selecting variables. All variables
selected in the Graph View when invoking the dialog will have the check-box checked. Conversely, allvariables with the check-box checked, will become selected in the Graph View when we exit the dialog.
Given that a practical model may contains many variables (we have seen in our work with clientsqualitative models consisting of hundreds of variables), locating and selecting them may be daunting.
The filter field above the list of data columns serves for selecting columns. Typing anything in the filter
field causes the dialog to limit the names of data columns to those that match the filter. For example,
typing "p" into the above dialog will lead to the following selection:

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Letter case is ignored, i.e., "p" is equivalent to "P". In addition to regular characters, the filter fieldinterprets wildcard characters, such as an asterisk (*) or a question mark (?) similarly to Windows.
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Any of the variables visible in the list can be selected or de-selected. Selection can be made by checking
the small check-box to the left of the variable name or by pressing the space key. All highlighted
variables will be selected or deselected by this. Right-clicking anywhere in the window brings up thevariable list context menu:

Check item (or pressing the space key) will change the value of the check-box: When it is check, it will
be unchecked and when unchecked, it will be checked.
Invert Checks will invert all checks on the list.

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Select All will highlight all nodes currently visible in the window. Subsequent pressing of the space key
will select or deselect all of them but will have no effect on the remaining (currently invisible) model
variables. There is no Select None choice in the menu but deselecting all nodes can be accomplished by
selecting all nodes and pressing the space key once or twice (if the first pressing leads to selection).
Finally, the interface allows for copying and pasting the list of selected variables. These operations
disregard the filter and copy/paste nodes from/to the entire model. Copy Checked Items places the listof the selected nodes (text format) on the clipboard. The clipboard format is multi-line text containing
the names of items in the list. If, at some later stage, we select and copy the text list outside of QGeNIe
and choose Paste Checked Items, QGeNIe will select only the variables on the list. Paste Checked Items
command will remove the current check marks, unless the SHIFT key is pressed when command is
invoked. This functionality is very convenient in case the list of variables is large and repetitive variableselection process is laborious.
Select by Type submenu offers additional possibilities for node selection:

Nodes, with a shortcut CTRL-SHIFT-A, selects all nodes in the current Graph View. Please note that
Select All selects all objects and that includes all submodels, arcs, text boxes, etc., while Nodes selects
only nodes.
Submodels (shortcut CTRL+ALT+A) selects all submodels in the current Graph View.
Arcs selects all arcs in the current Graph View.

5.5.7

Model navigation tools
QGeNIe includes several simple tools that facilitate model navigation. Each of them is described indetail in other parts of this document. This section lists them in order to expose them and their
purpose.

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Submodels
Very often, when a model is large, it becomes impossible to navigate through its graph - it may look
like a spaghetti of nodes and arcs. Luckily, real world systems and their models tend to exhibit ahierarchical structure (Simon, 1996). There may be several variables that are strongly connected witheach other and only weakly connected with the rest of the model. Such may be the case in a businessmodel - purchasing, production, and sales may be three almost autonomous subsystems that can beconnected with each other through a small number of links, their inputs and outputs. A decision maker
may want to examine each of these subsystems in detail, but may also want to have a global view of theentire business without unnecessary detail. We advise to use submodels whenever a model becomes
sufficiently complex.
One problem that a user will experience is navigation between submodels. To aid navigation, QGeNIe
allows to traverse the model by right-clicking on small triangles in those nodes that have parents orchildren in other submodels and locating these.

Tree View and Graph View
Models are typically developed, edited, and viewed in Graph View, which is the program's primaryview. Some operations, however, may be more convenient in the Tree View, which offers an alternative
to the Graph View. Nodes in the Tree View are listed alphabetically, so finding them may besometimes easier than locating them on the screen. The Tree View shows the submodel hierarchy andallows for moving nodes between various submodels. The two views work side by side, similarly to atree view and directory view in Windows.

Model as a document
Following the idea that one of the main goals of a model is documenting the decision making process,
QGeNIe supports two constructs that aid documenting the model: text boxes and annotations.
Text boxes allow for adding an arbitrary text to the background of the Graph View window. This text
may be useful as a comment explaining the details of the model.
Annotations, which are small yellow stick-it notes, which can be added to nodes and arcs,are useful for
explaining function of nodes and arcs between them, or to note down just about anything the user feels
is important regarding various model elements.

Text search
Find button and Find choice in the Edit Menu (described in the Graph View section) allows forsearching through the model for a text. It searches through all text elements of the model, such as IDs,
names, descriptions, annotations, text boxes, and displays a list of elements found. These elements canthen be located within the model.

Visualization of strength of relationships
Intuitively, interactions between pairs of variables, denoted by directed arcs, may have different
strength. It is often of interest to the modeler to visualize the strength of these interactions. QGeNIe
offers a functionality (described in the Strength of influences section) that pictures the strength of

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phase. Model builders or experts can verify whether the thickness of arrows corresponds to theirintuition. If not, this offers an opportunity to modify the parameters accordingly.

Status Bar and Output windows
Status bar tells about problems: The Status Bar command displays and hides the Status bar. The
Status bar is a horizontal bar located at the very bottom of the main QGeNIe window. For more
information on the Status Bar, see the Status bar section of QGeNIe workspace.
The Output command displays and hides the Output Window. For more information on the Output
window, see the Output window section of QGeNIe workspace.

5.5.8

In-degree analysis
Probabilistic inference is worst-case NP-hard, which is a Computer Science term for problems that are
possibly unsolvable with limited resources. When constructing models, you may encounter a situationin which the model becomes too complex for inference (QGeNIe will generally warn you about this in
the Output Window).
Problems with inference are typically caused by the complexity of connections in the graph. While it ishard to control complexity of connections, there is one way that it can be kept reasonably low - making
sure that nodes do not have too many parents. QGeNIe will warn you when you add more than 20
parents to a node but will not forbid you doing so. Nodes with fewer than 20 parents can also lead todifficulties with inference. There is also a dialog that allows you to view nodes by their number ofparents (this is called in the literature indegree). Choosing Show Nodes by Indegree from the Network
Menu:

shows the following dialog:

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Building blocks of QGeNIeThe user can get an idea which families (nodes and their parents) can give possible problems withinference. Reducing the number of parents of those nodes that have the highest indegree can help withmaking inference more efficient. To visit a node from the list in the Graph View, please double click on
the node or select on the list and press the Locate node button.

5.6

Saving and loading models in QGeNIe

5.6.1

Introduction
While QGeNIe is a general purpose decision modeling environment, it has been originally written withresearch and teaching environments in mind.

Opening a model in QGeNIeThere are three ways in which you can open a model in QGeNIe,· Choose Open... dialog from the File menu
· Click on Open ( ) button from the Standard Toolbar· Use the CTRL+O shortcut

Shown below is the Open... option in File Menu.

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The numbered list of files at the end of the File Menu are the Most Recently Used (MRU) file list. You
can click on any of those names to re-open the file.
Each of the three ways of invoking File Open dialog leads to the following:

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Building blocks of QGeNIeThe dialog that appears allows you to choose a file to load.
QGeNIe uses the QDSL file format, which is an XML-based format.
Note : You can have multiple files open at the same time.

Opening a QGeNIe model from a BayesBox-powered model
repository
QGeNIe supports loading models from BayesBox-powered repositories, which are web-based
repositories, possibly local and internal to user's organization. There are three ways in which you can
open a repository model in QGeNIe,· Choose Open from BayesBox... from the File Menu
· Use the CTRL+SHIFT+O shortcut· Press the Open network from BayesBox () button

Each of the three actions opens the Open from BayesBox dialog:

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Building blocks of QGeNIeThe default BayesBox instance is BayesFusion's model repository but any web address can be entered
here. GeNIe remembers the last successful connection to a BayesBox instance, which is convenient for
those users who rely on their internal BayesBoxes. Fetch allows to see models and categories present in
the chosen BayesBox instance:

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It is possible to traverse BayesBox's directory structure by clicking on the Category pop-up menu:

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Selecting a model from the list fetches the model from the current BayesBox instance and opens it in
QGeNIe.

Saving a model in QGeNIeThere are three ways in which you can save a file in QGeNIe:· Choose Save or Save As from the File menu
· Click on Save ( ) button from the Standard Toolbar· Use the CTRL+S (Save) shortcut

The difference between Save and Save As is that Save As lets you store the file under a different name,so that you can keep the original model file intact. Save will store the changes in the original file.
However, if you are working on a new file, then Save As is the only option and Save converts to SaveAs.

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5.6.2

File menu

The File menu offers the following commands:
New starts a new QGeNIe model and opens it in a new Graph View window. This command can bealso invoked by pressing the New () tool on the Standard Toolbar or using the CTRL+N shortcut.

Open... starts the Open dialog that allows you to open an existing model, been saved previously on the
disk. This command can be also invoked by pressing the Open (using the CTRL+O shortcut.

) tool on the Standard Toolbar orOpen from BayesBox opens an existing model from a BayesBox-powered model repository. This
command can be also invoked by using the CTRL+SHIFT+O shortcut.
Close closes all windows of the current model. If there are any changes to the currently opened modelthat have not been saved, QGeNIe will warn you using the following dialog box

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If you want to save the changes that you have made since you have last saved the model, click the Yes
button or press Enter. If you want to discard them, click the No button. If you have second thoughts
about exiting QGeNIe, click Cancel.
Save saves the currently opened model using the current file name and file format. If the document is
new (i.e., if it has never before been saved), this command is equivalent to the Save As... command.
This command can be also invoked by pressing the Save (the CTRL+S shortcut.

) tool on the Standard Toolbar or using

Save As... starts a Save As dialog that allows you to save the currently opened document to a newly
specified file.
The Open Network and Save/Save As commands are discussed in more detail in the introduction tothis section.
Export to GeNIe... opens a dialog that allows to save the current model as a GeNIe file. The main
reason why you might want to do this is a further refinement or examination of the model in a fully
quantitative setting. Many users start with a qualitative model, which is very easy to build, andsubsequently export the model to GeNIe to refine it further.

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Print... prints the current Graph View window. This command can be also invoked using the CTRL+P
shortcut.
The following dialog box allows you to modify some of the printing options, such as choose the printer,the range of pages to be printed, the number of copies to be printed, and other printer properties(through Properties... button).

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Print Preview displays the content of the current Graph view window on the screen as it would appear
when printed. When you choose this command, the main window is replaced with a print preview
window in which one or two pages will be displayed in their printing format. The print preview toolbar
offers you options to view either one or two pages at a time; move back and forth through the
document; zoom in and out of pages, and initiate the print job (bypassing the Print command).

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Print Setup... opens a dialog that allows for selecting the printer and printer and its properties,
including the paper size, source, and orientation. This command presents a Print Setup dialog box,
where you specify the printer and its connection. The appearance of the dialog box below may varydepending upon your system configuration.

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Building blocks of QGeNIeQGeNIe displays the names and disk locations of eight most recently used models. You can load themby choosing their names from the menu, bypassing the Open command.
Exit ends your QGeNIe session and exits the program.

5.6.3

QDSL file format
The XML Schema for QGeNIe's native QDSL file format can be found at the following location:
https://support.bayesfusion.com/docs/.

5.7

Inference algorithms

5.7.1

Introduction
The default QGeNIe algorithm for model updating, the clustering algorithm is exact, very efficient, andfast.
Because probabilistic reasoning is worst-case NP-hard, a QGeNIe user may encounter networks forwhich the memory requirements or the updating time may be not acceptable. In such a case, it may benecessary to choose a different algorithm. Even though we have never experienced the need in practice
to change the default algorithm, it is conceivable that the clustering algorithm will run out of memory
or will take too much time. In such a case, we recommend trying the Relevance-based decomposition
algorithm, which is also an exact algorithm making use of the structural properties of the model athand and with some overhead can make the model tractable. If this does not work, we recommend the
EPIS sampling algorithm, which is quite likely the most efficient stochastic sampling algorithm fordiscrete Bayesian networks. While it is an approximate algorithm, it shows excellent convergence rates
and should deliver precision that will be satisfactory. Tradeoff between precision and computation
time can be controlled by selecting the number of samples.

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5.7.2

Node menu
We have deferred the description of the commands Set Evidence, Clear Evidence, Control Value, Set
Focus and Clear Focus to the current section, which offers background information to Bayesian
network algorithms. We reproduce the Node menu, which is to a large degree duplicated by the node
pop-up menu, below:

Set Evidence submenu allows for setting the node state, which amounts to observation of that nodestate. The submenu is active only if there is one (and only one) node selected in the Graph View.

To select a state of the currently selected node, select this state on the submenu listing the states andrelease the mouse button. The state will have a check mark next to its name. The node will from thatpoint on be marked with a corresponding icon, indicating that one of the states of this node (True orFalse) has been observed. To change the node back to the unobserved state, choose Clear Evidence
from the Node menu. Setting evidence for a node can be accomplish in three other ways: (1) By
choosing Set Evidence in the node context menu, which is similar to the Node menu, (2) double
clicking on one of the state icons (T or F), or (3) pressing the letter T or F (for True and False) once thenode has been selected.

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Building blocks of QGeNIeControl Value submenu works precisely like the Set Evidence submenu but it stands for controllingrather than observing the value. Controlling means that the value has been set from outside. QGeNIe's
implementation of controlling the value follows so called arc-cutting semantics, which means that theincoming arcs of the controlled node become inactive (nothing inside the model influences the node, as
its value is set from outside). QGeNIe shows these inactive arcs as inactive by dimming them. See
Controlling values for more information about this functionality.
Clear Evidence command (for controlled nodes, this command is called Release Value) is active only ifthere are nodes selected in the Graph View and at least one of these node has been previously
observed (or controlled). Clear Evidence un-observes a state, i.e., it reverses the effect of the Set
Evidence command. Release Value un-controls a state, i.e., it reverses the effect of the Control Valuecommand. The check mark next to the state name will disappear. The status icon will be adjusted
accordingly.
The Set Focus command is active only if there is exactly one node selected in the Graph View. It allows
you to set the status of the selected node(s) to be the Focus. The node will from that point on beequipped with the Focus ( ) status icon, indicating that the node is the focus of reasoning. This isimportant in the value of information and value of control calculations.
The Clear Focus command is active only if the node selected is the focus. Clear Focus reverses theeffect of the Set Focus command. The Focus ( ) status icon will disappear.

5.7.3

Network menu
The Network menu allows for performing operations that relate to the entire network. It offers thefollowing commands:

Network Properties invokes the Network Properties sheet for the current model. The network
property sheet can also be invoked by double-clicking in any clear area on the main model Graph View
window. See Network properties section for more information.
Clear All Evidence allows for retracting all evidence in one simple step rather than doing it for eachindividual node.
Locate Focus Node finds the node designated as focus.

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Variable Arc Widths helps with analyzing the model structure by displaying links between nodes (thearcs) in variable width, corresponding to the magnitude of their influence.
Export Annotations collects all annotations, descriptions, and comments from the entire model andplaces them in a single text file that can be analyzed outside of QGeNIe.
Algorithm submenu allows for choosing the default algorithm for Bayesian networks:

The menu allows for choice of the default algorithm (marked with a bullet). Whenever updating takes
place, the current default algorithm will be executed.
Dynamic Models submenu groups all operations on dynamic Bayesian networks

This submenu is discussed in detail in the section of Dynamic Bayesian networks.

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5.7.4

Clustering algorithm
Clustering algorithm is the fastest known exact algorithm for belief updating in Bayesian networks. It
was originally proposed by Lauritzen and Spiegelhalter (1988) and improved by several researchers,e.g., Jensen et al. (1990) or Dawid (1992). Our implementation of the clustering algorithm is very
efficient and lightning fast. In fact, it is quite possibly the fastest implementation of this algorithm inexistence.
The clustering algorithm is the default algorithm used by QGeNIe. The clustering algorithm is
QGeNIe's default algorithm and should be sufficient for most applications. Only when networks
become very large and complex, the clustering algorithm may not be fast enough. In that case, it issuggested that the user choose an approximate algorithm offered by the program, the EPIS-BN
algorithm (Yuan & Druzdzel, 2003).

5.7.5

Relevance-based decomposition
Relevance-based decomposition is an exact algorithm based on the clustering algorithm that performs
a decomposition of the network when the network is very large. The algorithm was described in (Lin &Druzdzel, 1997). Relevance-based decomposition extends the boundary of what is computable, whilegracefully changing into the clustering algorithm for small networks. Because there is some overhead
related to decomposition, we suggest that this algorithm be used only when the clustering algorithm
cannot handle your networks.

5.7.6

EPIS Sampling
The Estimated Posterior Importance Sampling (EPIS) algorithm is described in (Yuan & Druzdzel
2003). This is quite likely the best stochastic sampling algorithm for discrete Bayesian networks
available. It produces results that are even more precise than those produced by the AIS-BN algorithm
and in case of some networks produces results that are an order of magnitude more precise. The EPISBN algorithm uses loopy belief propagation to compute an estimate of the posterior probability over all
nodes of the network and then uses importance sampling to refine this estimate. In addition to being
more precise, it is also faster than the AIS-BN algorithm, as it avoids the costly learning stage of thelatter.

5.8

Keyboard shortcuts
File operations
CTRL+N: Create a new network
CTRL+O: Open an existing network
CTRL+P: Print the current network
CTRL+S: Save the currently active file to disk
CTRL+W: Close the current network

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Layout of elements in the Graph View
CTRL+G: Toggle display of grid lines
CTRL+L: Highlight selected model elements
CTRL+SHIFT+G: Toggle auto alignment of elements to grid
F8: View nodes as bar charts
SHIFT+F8: View nodes as icons

Finding / selecting nodes / spreadsheets
CTRL+F: Find a node
CTRL+A: Select all elements
CTRL+SHIFT+A: Select all nodes
CTRL+ALT+A: Select all submodels

Show / hide windows
CTRL+T: Toggle display of the Tree View window
CTRL+U: Toggle display of the Output window
CTRL+ALT+C:
F11: View network full-screen (hides all views, menus, and toolbars)
F12: Zoom to fit window
CTRL+F12 / CTRL +*: Zoom to 100%
CTRL+PLUS: Zoom in
CTRL+ MINUS: Zoom out

Renaming objects

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F2: Rename object

Editing
CTRL+B: Bold font
CTRL+I: Italic font
CTRL+C: Copy
CTRL+V: Paste
CTRL+X: Cut

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Using QGeNIe
6

Using QGeNIe

6.1

Introduction
This section presents various modules of QGeNIe from the point of view of their function. It is analternative view to the one presented in the previous section, which focused on QGeNIe's building
blocks.

6.2

Applications of qualitative probabilistic modeling
A qualitative modeling environment like QGeNIe has many applications. In this section, we give three
example of applications that we have encountered.

A. Rapid Prototyping
Even if the goal is to build a fully quantified Bayesian network, QGeNIe is extremely valuable as anenvironment for rapid prototyping. Models developed quickly within QGeNIe can be exported to
GeNIe for further elaboration and parameter refinement.

B. Modeling in “Soft Sciences”
As late Herb Simon convincingly argued (Simon 1996), “soft sciences” are really hard sciences. Models
of social sciences systems are really hard to build, because very often little is known about them. Still,there is ample evidence from behavioral decision theory (e.g., Dawes, 1988) that even simplest
mathematical models typically perform better than unaided human intuition. QGeNIe has been
applied to projects that are truly hard to tackle with formal methods. One example that we are aware ofis an application in modeling security in Costa Rican cities (Pérez-Liñán, 2008). The screen shot below
shows a simple QGeNIe model used in the study.

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Using QGeNIeC. Group Decision Making
The original inspiration for QGeNIe was provided by our collaboration with policy analysts at theUnited States Naval War College, Bradd Hayes and Theo Gemelas. A model developed there (see thescreen shot below, the foundations for this model were developed in (Hayes & Sands, 1997), Figure 5-1,
page 101) consisted of 99 variables organized into 12 submodels. The goal of this model was to bring
together experts from a variety of areas relevant to stability of a region (in this case, the Black Sea
region). The experts know some aspect of the problem (e.g., economy, culture, or energy) but not
everything. They may help with building various parts of the model. Asking “what if” questions of thecomplete model allows each of the individual experts to verify their intuitions but also see how
manipulations propagate through those submodels that they did not know much about. A model ofthis complexity cannot typically be understood completely by a single expert. Presence of multiple
experts in a room, each of whom is familiar with a parts of the model, usually helps with verifying
model assumptions and obtaining insight into the problem and consequences of the resulting
decisions.

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6.3

Bayesian networks

6.3.1

Building a qualitative Bayesian network
Building a qualitative Bayesian network in QGeNIe is demonstrated step for step in section Hello
QGeNIe!

6.3.2

Simple interaction patterns
The DeMorgan model of interactions between a variable and its parents is flexible and allows formodeling a wide variety of possible interaction patterns. In this section, we show two simple, popularinteraction patterns: Noisy-OR and Noisy-AND, familiar to those modelers who have worked withcanonical models in the context of Bayesian networks.
The Noisy-OR interaction takes place when all links from parents to the child node are Causes.
Consider the following example:

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Using QGeNIeThe values of prior probabilities are shown in the parents' prior marginal distributions, the value ofstrengths of influences are marked on the arcs, the value of Leak in the node Jaundice is 0.01. The
model reproduces precisely the NoisyOR.xdsl model in BayesFusion's interactive model repository.
The Noisy-AND interaction takes place when all links from parents to the child node are
Requirements. Consider the following example:

The values of prior probabilities are shown in the parents' prior marginal distributions, the value ofstrengths of influences are marked on the arcs, the value of Leak in the node Accident is 0.95. The
model reproduces precisely the NoisyAND.xdsl model in BayesFusion's interactive model repository.
Introduction of Barriers in Noisy-OR models is straightforward: The resulting model will be still a
Noisy-OR but the parents' (Barriers') states can be viewed as reversed. The same happens when we
introduce Inhibitors in the Noisy-AND model: The resulting model will be still a Noisy-AND but theparents' (Inhibitors') states can be viewed as reversed. Mixing all four types of causal influences in one
model makes the model no longer a pure Noisy-OR or a Noisy-AND but a formally correct
combination of noisy OR and AND functions that allows for modeling a complex interaction ofparents.

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6.3.3

Entering and retracting evidenceEntering observations (evidence) is one of the basic operations on a probabilistic model. It amounts toadjusting the model to a new situation, one in which more information is available. It allows to query
the system subsequently about the new, posterior probability distributions. If you have gone throughBuilding a Qualitative Bayesian network, you might remember that you have already entered evidence
for the Hot Weather and Climate Control in the Building nodes. Let us go through this again.
You may load the model Productivity.qdsl created in Building a Qualitative Bayesian network from the
Example Networks folder.
To enter evidence into your model, right-click on the node in question (in the picture below, node
Climate Control in the Building) and choose Set Evidence.

Alternatively, select the node in question and choose Set Evidence submenu from the Node Menu.

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Yet another way of entering evidence is double-clicking on one of the small square icons () in thelower-right corner of a node. In the image below, we double-clicked on the icon False ( ) in both
Climate Control in the Building and Hot Weather nodes.

Finally, perhaps the simplest way of entering evidence is pressing the letter T or F (for True and False
respectively) once the node has been selected.
To retract evidence, double-click on the bold icon in a node with previously entered evidence ( )shown in the image above, press the letter C (for Clear) once the node has been selected, or right-clickon the node and choose Clear evidence.

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Alternatively, select the node in question and choose Clear evidence from the Node Menu.

You can also retract all evidence by choosing Clear all evidence from the Network menu:

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To enter different evidence instead of retracting evidence altogether, just double click on a different
state icon () or set different evidence from the Set Evidence sub-menu.

6.3.4

Controlling values
In causal probabilistic models, there is an additional class of inference problems: Predicting the effects
of external intervention. In the context of Bayesian networks, computing the effects of observations
amounts to belief updating after setting evidence for the observed network variables. The effect ofintervention, on the other hand, is a change in the network structure, related to external manipulationof the system modeled by the network, followed by setting the values of the manipulated nodes andupdating beliefs.
We will explain control values with the help of the following example (the model, Project.qdsl, is
included among the example models) describing a set of variables that may influence profits of an
airplane manufacturer.

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Imagine that we are conducting this analysis at the very initial stage of negotiations with the client andwould like to know the impact of negotiating in the contract that no penalties can be imposed on the
manufacturer. This is a typical question involving controling the values of variables. No matter what
happens, we impose zero penalties on the system, which means that we impose the probability of zeroon penalties. To predict the effect of this manipulation, we right-click on the node Penalties and choose
Control Value.

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The result of this operation is that all arcs ending at the controlled node are dimmed. This denotes thatfact that none of the usual causes of this variable are operational - the value of this variable ismanipulated and determined outside of the model.

The node Penalties is marked by the manipulation icon ( ), which means that the variable iscontrolled from outside of the model.
The next thing to be done is making sure that the probability of penalties is zero. We can achieve thisby observing the value of Penalties to be False. This can be done in the reverse order, i.e., firstobserving the value of Penalties to be False and then controlling the variable Penalties.
It is important to realize that while controlling a variable is a change in the structure of the model, thischange is temporary and that controlling a variable leads to a query similar to observing a variable,
although its consequences are different. Since the state of a controlled node does not depend on thevalue of its parents, there is a temporary change in the network structure. QGeNIe shows this bydimming the arc connecting the parent nodes to the controlled node.

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Notice that the intervention only changes the posterior probabilities of the descendants of thecontrolled node. The control value operation is not available for those nodes that have observed ormanipulated descendants. Controlling the value of a descendant of an observed node would lead to atheoretical problem, which one could summarize briefly as a desire to modify the past.

6.3.5

Viewing results
QGeNIe is all about exploring models and viewing results of calculations after observing evidence andperforming manipulations. There are several ways of viewing interesting information from a model.

Probabilities of various propositions
Probabilities of propositions can be viewed in several ways. The easiest, one that does not require any
action, is observing the colors of nodes, which represent probabilities of truth (of falsity -- this can be
set in node properties) of the proposition represented by the node. The Productivity model, used in the
Building a qualitative Bayesian network section, shown below, shows the probabilities of each of thefour propositions represented by the four model variables

Climate Control in the Building is marked as observed to be false and it is also bright
red, showing that the probability of the climate control in the building is zero (either
because of absence of an AC unit or because it is broken). Hot Weather is marked as
observed to be false and it is also bright green (please note that the colors for thenode Hot Weather are reversed, so green denotes falsity of the proposition), showing
the the probability of the weather being hot is zero. High Productivity, the focus
variable in the model, has a probability between 0.5 and 1.0 judging by the nodecolor. The small circle in the upper part of the model shows probability 0.5 and 1.0 by
its white and intensive green halves. Clearly, the node color is closer to green than it
is to white.
It is possible to see the exact numerical values of the probabilities by opening the properties of any
node. Let us open the properties of the node High Productivity:

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The Value tab shows the probability of truth of High Productivity is 0.74929816.

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You can also right-click on the node and choosing Set Evidence. GeNIe shows a list of states of thenode along with their probabilities, when these are available.

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Using QGeNIeThere are other model elements that belong to broadly-understood results that you can view through
QGeNIe interface. We will discuss these in the following sections of the manual: Structural analysis,
Value of information/manipulation, and Inference in DBNs.

6.3.6

Structural analysis
One of the important elements of probabilistic modeling is the ability of directed probabilistic graphs
to represent the causal structure of the modeled domain. The structure itself is very valuable and is an
important source of insight. Models built by means of QGeNIe can be examined structurally. An
important element of this analysis is the structure itself, viewing the strengths of influences andpathways through the graph. This section describes tools for the analysis of the graph structure.

Dimming unnecessary arcs

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Dimming unnecessary arcs, discussed earlier in this manual, is a simple tool that allows for finding one
class of modeling errors, errors of omission. One such error is having an influence arc that has zero
numerical effect. QGeNIe shows such arc as dimmed, as they are unnecessary and are quite likely the
result of an error. Whenever you see a dimmed arc, please have a look at the child node and its parents- chances are that it is not that the arc is unnecessary but rather that you have forgotten to set thenumerical value of influence of the parent node on the child node.

Locating disconnected nodes
When modeling, many a user create a collection of nodes and then connect them by means of arcs.
When the model under construction is sufficiently large, it is not uncommon to forget about some of
the nodes and leave them unconnected to the rest of the network. Locating disconnected nodes is afunctionality that helps to find such nodes so that we can give them more attention. To finddisconnected nodes, please select Select Disconnected Nodes from the Edit Menu:

If there are no disconnected nodes in the model, this choice is going to be grayed out. Nodes selected
can be subsequently highlighted (choice Highlight Selection or Ctrl-L in the Edit Menu) and easily
located in the Graph View visually.

Strength of influence
Clicking on the Enable variable arc widths () tool from the Standard Toolbar changes theappearance of the arcs in the network. The arc have different thickness, dependent on the strength of
influence between the nodes that they connect. Strength of influence is based on the arc weightparameters, entered during the construction phase of the model.
Here is a fragment of the Project network with the Enable variable arc widths tool pressed.

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If the mouse is placed on the head of the arrow, information relating the strength of influence is shownin a comment box. The image below shows the strength of influence of the arc between Technical
Problems and Further Tests Needed.

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Variable arc widths allows for structural analysis of the model, as the show the strength of influences
and, by this, the most critical paths of information flow. They allow for a visual verification of theintuitions that are captured in the model. The above screen shot of the Project model show, forexample, that the most critical pathway influencing profits is through customer demands for increased
load and higher speed, which may result in technical problems, need for further tests, budgetary
problems, time delay, and penalties, resulting in decreased profits.

Neighborhoods
There are several useful functions that help with analysis of connections and pathways through thequalitative graph. One group of such functions is showing connections of a selected node. To open theconnections dialog, please select Show Connections from the context menu of the node in question.
The image below show invoking the dialog for the node Seriously Over Budget.

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Using QGeNIeThe dialog allows for selecting parents (direct predecessors in the graph), children (direct successors in
the graph), ancestors (all predecessors in the graph), descendants (all successors in the graph), and thenode's Markov blanket (the set of nodes that make the node in question independent of the othernodes in the graph). The only parent of the node Seriously Over Budget in the graph is Further Tests
Needed.

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Further Tests Needed is also a part of the ancestors of the node Seriously Over Budget.

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Seriously Over Budget has two children: Negative Profit and Project Failure.

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They both belong to the descendants of Seriously Over Budget.

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Using QGeNIeParents, children, and the parents of those children belong to the Markov blanket of the node
Seriously Over Budget.

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Using QGeNIeThe dialog allows for traversing the graph through changing the focus of the analysis. A new focus canbe chosen by selecting it from the list of nodes and pressing the Switch POV (Point of View) button in
the lower-right corner of the dialog. Let us select Technical Problems and press the Switch POV
button.

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Pressing the Switch button leads to refocusing the neighborhood to the node Technical Problems.

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Selecting any of the nodes on the list and pressing Locate node button locates the node in the Graph
View and flashes the node three times. Double-clicking on the selected node has the same effect.
Selecting Further Tests Needed and pressing the Locate node button yields:

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Pressing the Highlight all button exits the dialog and highlights all nodes on the list. In the image
below, Highlight all button was pressed when the Node connections dialog showed the Markov
blanket of the node Technical Problems.

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Pressing ESC or choosing Clear Highlight from the network pop-up menu will clear the selection.

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Pressing Select button in the Show Connections dialog selects all nodes on the list (selection does notinclude the POV node!) in the Graph View. In the image below, Select button was pressed when the
Node connections dialog showed the Markov blanket of the node Technical Problems.

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Using QGeNIeThis selection can be further enhanced through other ways of selecting nodes and arcs. Please see theSelect Nodes... dialog, described in the Selection of model elements section.

Pathways of information flow
QGeNIe allows for showing active paths through which information flows between a pair of nodes. To
show all paths between two nodes A and B, please select these two nodes and choose Highlight Paths
from the node context menu of one of the two nodes. The following image shows how to invoke
highlighting paths between the nodes Increased Load and Negative Profit.

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The result of choosing Highlight Paths above is

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Pressing ESC or choosing Clear Highlight from the network pop-up menu will clear the selection.

6.3.7

Case Manager
QGeNIe includes a Case Manager window that allows users to save a partial or a complete session as acase and retrieve this case at a later time. Cases are saved alongside the model, so when the model isloaded at a later time, all cases are going to be available. Case Manager window can be opened
through the View Menu:

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Case Manager window looks initially as follows:

Adding cases to the Case Manager
Let us add to the Case Manager the three cases analyzed in the Building a qualitative Bayesian
network section of this manual. The first case involved failure of climate control on a cold day. Oncethe evidence in the model has been set, we click on the Add new case button (following dialog, which allows for entering case details.

QGeNIe Modeler). This results in the

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Once we click OK, the case is visible in the Case Manager:

Adding two remaining cases to the Case Manager will result in the following window.

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Using QGeNIeThe Case Manager window shows the currently applied case with a grayed background.

The Show only evidence nodes button () reduces the number of columns displayed to those onlythat have any observations at all. The Apply case (Graph View window. The Delete case button () button allows for transferring a case to the) removes the current case from the Case Manager.

The Update applied case with network evidence (from the current model to the current case.

) button transfers the current set of evidence

Exporting case records to a data file
Currently, QGeNIe does not allow to export cases from a model to a data file. However, it is possible toretrieve cases using any text editor from the model file (.qdsl). To locate the case descriptions, pleasesearch for the tag <cases> in the XML model file.

6.4

Most Effective Actions calculation

6.4.1

Introduction
It is often of interest to a user which variables can be best manipulated in order to affect a specified
node most. To this effect, QGeNIe allows for specifying a Focus node, denoted by a small target icon on
the graph. Given a focus node, QGeNIe supports calculating the value of information flowing fromobservations and the value of intervention (also known as manipulation of control). For that
calculation to be performed, the model builder should specify for every node in the model whether it ispossible to observe it and whether it is possible to manipulate it. Observable and Manipulable
properties are part of the General property sheet.

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Using QGeNIeThe Technical Problems node pictured above is Observable but not Manipulable.
The following two sections show how to enable value of information and value of intervention
calculation and how to perform and interpret its results.

6.4.2

Enabling Most Effective Actions calculation
To enable the most effective actions dialog, choose Most Effective Actions from the View menu

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Alternatively, use the shortcut CTRL-M or press the Toggle Most Effective Actions window (tool, which will all open the Most Effective Actions window

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6.4.3

Most Effective Actions calculation
Specifying for every node whether it is observable and manipulable and enabling the Most Effective
Actions calculation will all open the Most Effective Actions window

Pressing the Recalc () button or, better yet, pressing on the Autoupdate () button, willcalculate the value of observations and manipulations of those variables in the model that can beobserved or manipulated.

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We can look only at those variables that are observable

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or manipulable

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It is important to remember that the value of information/manipulation does not have units. It isbased on calculation of cross-entropy and just expresses the expected change in entropy of the model
Focus variable. The most valuable in this calculation is the relative magnitude of the calculated values
and their order.

6.5

Dynamic Bayesian networks

6.5.1

Introduction
A Bayesian network is a snap shot of the system at a given time and is used to model systems that are
in some kind of equilibrium state. Unfortunately, most systems in the world change over time andsometimes we are interested in how these systems evolve over time more than we are interested intheir equilibrium states. Whenever the focus of our reasoning is change of a system over time, we needa tool that is capable of modeling dynamic systems.
A dynamic Bayesian network (DBN) is a Bayesian network extended with additional mechanisms thatare capable of modeling influences over time (Murphy, 2002). We assume that the user is familiar with
DBNs, Bayesian networks, and QGeNIe. The temporal extension of BNs does not mean that the
network structure or parameters changes dynamically, but that a dynamic system is modeled. In otherwords, the underlying process, modeled by a DBN, is stationary. A DBN is a model of a stochastic
process. The implementation of DBNs in QGeNIe is based on (Hulst 2006).

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6.5.2

Creating DBN
We will use QGeNIe to create a qualitative model of a time-dependent domain focusing on a
manufacturer's decision whether to produce a high quality product and whether this will have effect on
profits and market share.
We start modeling with enabling the Temporal Plate, which is a special construct in the Graph View
that allows for building dynamic models

The effect of enabling temporal plate in the Graph View is the following

The Temporal Plate divides the Graph View into four areas:

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Using QGeNIe· Contemporals, which is the part of the Graph View window that is outside of the temporal plate.

All nodes outside of the Temporal Plate are static.
· Init Conditions, which is the part of the network area where, so called, anchor nodes are stored.

An anchor node is a node outside of the temporal plate that has one or more children inside thetemporal plate. Anchor nodes are similar to static nodes outside of the temporal plate but they
are only connected to their children in the first time-slice of the network.
· Temporal Plate, which is the main part representing the dynamic model. Nodes in the

Temporal Plate are the only nodes that are allowed to have Temporal Arcs. This area also shows
the number of time-slices for which inference will be performed.
· Term Conditions, which is the part of the network area where the terminal nodes are stored. A

terminal node is a node outside of the temporal plate that has one or more parents inside thetemporal plate. Terminal nodes are only connected to its parents in the last time-slice of the
network.
The size of the Temporal Plate can be changed by clicking and dragging its edges and so can the sizesof its three areas (Init Conditions, Temporal Plate, and Term Conditions). There is a small subtlety inresizing the three. If you click and drag the extreme right or extreme left edge of the temporal plate, itis the middle part (the Temporal Plate) that gets resized and the sizes of Init Conditions and Temporal
Plate remain the same. Pressing the SHIFT button when dragging the edges has the effect that the sizeof the Temporal Plate remains the same and the sizes of Init Conditions and Temporal Plate change.
For our example, we set the number of steps to 24. We can either double-click or right-click on theheader of the Temporal Plate

which will invoke the Time Step Count dialog that allows to change the Time stepcount

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Time steps have no units and are a matter of interpretation of the modeler. In our model, we caninterpret the time step as one month but in any other model it could mean a year, a day, or amillisecond.
We create the following nodes: High Quality of the Product, Product Reputation, High Production
Cost, Aggressive Advertizing, High Price, High Sales, High Profits, Large Market Share, andCompany's Reputation in the Temporal Plate and the Company's Current Reputation node in the Init
Conditions area.

The next step is to connect these nodes. There are two types of arc between nodes: normal arcs andtemporal arcs. Let us first create an arc between Aggressive Advertizing and High Production Costs.
We click on the Arc button ( ), subsequently click on the node Aggressive Advertizing and drag andrelease the mouse inside the node High Production Costs. A menu pops up.

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We select Normal arc, which creates an arc between Aggressive Advertizing and High Production
Costs. and show the familiar arc type dialog

We select Cause for this interaction, which results in a normal (static) arc between the two variables.
The next arc to add will be a temporal arc between the nodes Product Reputation and Aggressive
Advertizing. We start again with clicking on the Arc button ( ), subsequently clicking on Aggressive
Advertizing and dragging and releasing the mouse inside the node Product Reputation.

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Using QGeNIeThis time we want to indicate that the influence of Aggresive Advertizing on Product Reputation is
not immediate but takes one time step. We select a (temporal) Order 1 arc that we subsequently
designate as a Cause in the arc type dialog.

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We continue adding arcs between nodes, quantify them (the model Product Temporal.qdsl can befound among the example models) and create the following structure

There is one type of temporal arcs inside the Temporal Plate that was not allowed in static models,
namely arcs starting and ending at the same node and, by this forming cycles. Cycles represent

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temporal processes and are allowed only for temporal arcs. Similarly to static networks, normal arcs
are not allowed to form cycles, even inside temporal plates. Arcs in Temporal Plate can start and end
in the same node. We create such arcs by starting and ending the drawing in the same node. These arcs
essentially mean that the state of the variable in question at time t influences its state at time t+1. For
example, reputation gained (Product Reputation and Company's Reputation) tends to stick. Dynamic
models allow for cycles. For example, we have that Product Reputation influences Company's
Reputation and Company's Reputation influences Product Reputation. The interpretation of suchcycles is simple: Any temporal influence links variable in two different time slices and when theseslices are unrolled, there are no cycles.
Please note there while most of the temporal arcs are labeled with [1], meaning that they are temporal
influences of the first order, there is one arc in the model (from Product Reputation to Company's
Reputation) that is labeled [2], representing a temporal influence of order 2. QGeNIe is unique among
the existing Bayesian network software in that it allows for any order influences, which means that
DBNs in QGeNIe can model dynamic processes of any order.
Quantification of this model proceeds in the same way as quantification of static models (please see thesection on Building a qualitative Bayesian network for examples).
This concludes the creation and specification of the DBN modeling the problem. There is another wayof creating a DBN. Rather than constructing it directly in the Temporal Plate, we can construct a BN in
the Graph View window, drag it into the Temporal Plate, and then add temporal links. This has to bedone cautiously, as the order of dragging can make a difference. For example, if we drag a node into
the Temporal Plate, QGeNIe will remove all its outgoing arcs, as it is not allowed to have arcs from
temporal plate enter nodes in the Contemporals section. To avoid that, we can best drag entire groups
of nodes into the temporal plate, in which case no links will be deleted.
In the next section, we will show how to use dynamic models to obtain insight into the modeleddomain.

6.5.3

Inference in DBNs
Inference in a DBN, similarly to inference in a BN, amounts to calculating the impact of observation ofsome of its variables on the probability distribution over other variables. The additional complicationis that both evidence and the posterior probability distribution is indexed by time. We will go throughthe example used in the previous sections to demonstrate setting evidence, running an algorithm, andviewing the results.

Setting temporal evidence
We can enter into the Product Temporal.qdsl model evidence stating that the initial six months of thetime horizon the product s going to be of poor quality. This means that the evidence vector for the nodeHigh Quality of the Product is as follows:High Quality of the Product[0:6]= [false, false, false, false, false, false].
To enter this evidence, we right-click on the High Quality of the Product node and select Evidence...

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This invokes the Dynamic Evidence dialog

We enter the evidence vector as specified above:

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Viewing results: Node gradients
Once we exit the Dynamic Evidence dialog, QGeNIe calculates the impact of the observations. The
following screen shot of the model shows how the truth of the propositions represented by the nodes
changes with time.

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We can see that the first six months of poor quality product resulted in lower production costs but also
in lowering the reputation of the product and lowering the company's reputation.

Viewing results: Active time step
There is an additional, way of observing the results. QGeNIe allows for walking through the time steps
and observing how the colors of nodes change. This is invoked by pressing the Active time step icon (). This shows a small slides for walking through the time steps.

As we move the slider, the colors of nodes change. The following screen shot shows the colors (i.e.,
probabilities) at t=6. The time step is displayed in the header line of the temporal plate.

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Viewing results: Value tab
We can view the temporal beliefs in the Value Tab of a node as a spreadsheet indexed by the time
steps. In addition to the numerical values of the marginal posterior probabilities over time, we canview the results graphically. Pressing the Area chart (probabilities graphically:) button displays the posterior marginalPressing the Contour plot ( ) button displays the posterior marginal beliefs as a contour plot withprobabilities displayed by colors. Hovering over individual areas shows the numerical probabilities
corresponding to the areas/colors.

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Finally, pressing the Time series ( ) button shows the posteriors as a time series plot (a curve foreach of the two states of the variable):

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Viewing results: On-screen bar charts
Marginal posterior probabilities can be also shown on the screen permanently by the changing thenode view to Bar chart. This can be accomplished by selecting nodes of interest and then changing theview of the nodes through the View as/Bar Chart option.

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Using QGeNIeThe Bar chart view allows for displaying the temporal posterior marginal probabilities on the screen
permanently.

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Viewing results: Temporal beliefs
Marginal posterior probabilities can be also shown as a plot in a separate window called Temporal
Beliefs. The Temporal Beliefs window can be opened by selecting it on the View menu.

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Using QGeNIeThis opens the Temporal Beliefs window with the probability of the Focus node (in our model, it is thenode Large Market Share) displayed

We can select propositions for which probabilities we want displayed by pressing the Select outcomesbutton on the top on this window. This pens a dialog that allows for selecting individual node states.

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As we select states to be displayed, the Temporal Beliefs window shows them

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Using QGeNIe

Unrolling the DBN
As we mentioned above, for the purpose of inference, QGeNIe converts the DBN into a Bayesian
network and updates the beliefs using the selected belief updating algorithm. It can be useful, forexample for model debugging purposes, to explicitly unroll a temporal network. QGeNIe provides thispossibility through the Network-Dynamic Models-Unroll option.

QGeNIe creates a new network that has the temporal network unrolled for the specified number oftime-slices. It is possible to locate a node in the temporal network from the unrolled network by rightclicking on the node in the unrolled network and selecting -> Locate Original in DBN from the contextmenu. The unrolled network that is a result from unrolling the temporal network is cleared from any
temporal information whatsoever. It can be edited, saved and restored just like any other static
network. Figure below shows the unrolled network representation of a temporal network and how theoriginal DBN can located back from the unrolled network.

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Resources
7

Resources

7.1

Books
A good source of elementary knowledge of Bayesian networks is the path-breaking book by Pearl(1988). A good, thorough synthesis of the theoretical aspects of Bayesian networks and probabilistic
decision support systems, useful for readers interested in building a reasoning system from the groundup, is the book by Neapolitan (1990), regretfully out of print. Jensen's (1996) book is heartily
recommended as a good source of knowledge for both builders and users of Bayesian reasoning
systems. A more recent addition to the books on graphical modeling is Cowell, Dawid, Lauritzen &Spiegelhalter (1999), a heartily recommended reading for anybody who wants to develop a thoroughunderstanding of the methods that are at the foundation of graphical probabilistic systems.
de Finetti (1970) and Savage (1954) are recommended for the foundations of Bayesian probability
theory and decision theory.
Readers interested in practical aspects of decision support, especially in the context of policy making,are recommended the superb book by Morgan & Henrion (1989).
Russel an Norvig (1995) is a good textbook covering application of probabilistic methods in Artificial
Intelligence.
For readers interested in graphical probabilistic models in general, the thorough book by Whittaker(1989), covering directed and undirected probabilistic graphs, may be of interest.
An up to date list of textbooks covering the field of decision analysis can be found on BayesFusion's
web site.

7.2

Research papers
While there a good number of excellent papers covering the topic of decision-analytic decision support,here are some of our favorites.
An introductory paper on Bayesian networks, useful for beginners is (Charniak, 1991).
Overview papers by Horvitz et al. (1988), Cooper (1989), Henrion et al. (1991), Spiegelhalter et al.
(1993) and Matzkevich & Abramson (1995) are accessible introductions to the use of probabilistic anddecision-analytic methods in decision support systems.
Users interested in practical applications of Bayesian networks are directed to the March 1995 special
issue of the Communications of the ACM journal, edited by Heckerman, Mamdani and Wellman(1995).
(Henrion, 1987) is a manifesto arguing convincingly for the use of probabilistic methods in artificial
intelligence.

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Resources(Henrion, 1989) is a practical introduction to problems related to building probabilistic models.
Another place to look at is a special issue of the IEEE Transaction of Knowledge and Data
Engineering journal on building probabilistic models (Druzdzel & van der Gaag, 2000).

7.3

Conferences
Probably the best source for the state of the art research in graphical probabilistic models areproceedings of the annual Conference on Uncertainty in Artificial Intelligence (UAI). Proceedings of
UAI conferences are available electronically from Decision System Laboratory's web site located athttps://dslpitt.org/uai/
A similarly prestigious conference on the topic of probabilistic graphical models is the bi-annual
European Conference on Probabilistic Graphical Models (PGM). It brings together researchersinterested in all aspects of graphical models for probabilistic reasoning, decision making, and learning.
The web site for the 2018 conference, listing web sites for all previous PGM conference and their online proceedings, is at the following location:
http://pgm2018.utia.cz/
Another conference devoted completely to Bayesian networks is the Annual Conference of theAustralasian Bayesian Network Modelling Society (ABNMS). The web site for the 2019 conference,listing web sites for all previous ABNMS conference is at the following location:
http://abnms.org/conferences/abnms2019/
Other relevant conferences are AAAI National Conferences on Artificial Intelligence (AAAI),
International Joint Conferences on Artificial Intelligence (IJCAI), Conferences on Knowledge
Discovery and Data Mining (KDD), Workshop on Artificial Intelligence and Statistics, Uncertainty
Track of the Florida Artificial Intelligence Conferences (FLAIRS), European Conference on Symbolicand Quantitative Approaches to Reasoning and Uncertainty (ECSQARU), International Conferenceon Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU),
and European Conference on Artificial Intelligence (ECAI).

7.4

Model repository
An important class of World Wide Web resources that may be of interest to QGeNIe users are model
repositories. They are a great inspiration to model builders and source of models for the purpose of
testing algorithms. Here is where you can find BayesFusion's model repository:
https://repo.bayesfusion.com/
The repository is interactive and allows for entering evidence and performing Bayesian updating.
Furthermore, you should be able to download any of the models included in the repository to a local
disk and open it with QGeNIe. BayesFusion's interactive model repository is powered by BayesBox,
another BayesFusion's product, allowing our clients to create local and secure model repositories.

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Resources
7.5

Social Media
Please visit BayesFusion's YouTube channel for useful recordings that may help you in learning more
about decision-theoretic methods in intelligent systems, GeNIe and QGeNIe.
We use BayesFusion's Twitter account to communicate with our users important news, such as new
software releases. Following us on Twitter will ensure that you are up to date on what is happening at
BayesFusion.
BayesFusion's Facebook account contains important news releases. We make sure that important news
that are listed on our web site find their way to our Facebook page.
BayesFusion's LinkedIn account contains basic information about BayesFusion. We have started a
LinkedIn group GeNIe Users that is an open forum for our uses.
Finally, BayesFusion Forum is a good place to interact with us and other GeNIe and SMILE users.

7.6

References
Charniak, Eugene (1991). Bayesian networks without tears. AI Magazine, 12(4):50-63.
Cooper, Gregory F. (1989). Current research directions in the development of expert systems based on
belief networks. Applied Stochastic Models and Data Analysis, 5(1):39-52.
Cooper, Gregory F. (1990). The computational complexity of probabilistic inference using Bayesian
belief networks. Artifical Intelligence, 42(2-3):393-405.
Cowell, Robert G., A. Philip Dawid, Steffen L. Lauritzen & David J. Spiegelhalter (1999). Probabilistic
Networks and Expert Systems. Springer-Verlag New York, Inc.: New York, NY.
Dagum, Paul & Michael Luby (1993). Approximating probabilistic inference in Bayesian belief
networks is NP-hard. Artificial Intelligence, 60(1):141-153.
Dawes, Robyn M. (1988). Rational Choice in an Uncertain World. Hartcourt Brace Jovanovich,
Publishers.
Dawid, A. Philip (1979). Conditional independence in statistical theory. Journal of the Royal
Statistical Society, Series B (Methodological), 41:1-31.
Dawid, A. Philip (1992). Applications of a general propagation algorithm for probabilistic expert
systems. Statistics and Computing, 2:25-36.
de Finetti, Bruno (1970). Theory of Probability. John Wiley and Sons, New York.

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Resources
Marek J. Druzdzel (2009). Rapid modeling and analysis with QGeNIe. In Proceedings of theInternational Multiconference on Computer Science and Information Technology (IMCSIT-2009),
Mragowo, Poland, pages 101-108.
Diez, F. Javier & Marek J. Druzdzel (2006). Canonical probabilistic models for knowledge
engineering. Technical Report CISIAD-06-01. UNED, Madrid, 2006 (available at
http://www.ia.uned.es/~fjdiez/papers/canonical.html).
Druzdzel, Marek J. & Linda C. van der Gaag (2000). Building probabilistic networks: `Where do thenumbers come from?' Guest editors' introduction. IEEE Transactions on Knowledge and Data
Engineering, 12(4):481-486.
Eades, P. (1984). A heuristic for graph drawing. Congressus Numerantium, 41, page 149–160.
Hayes, B. C. and J. I. Sands (1997). Doing windows: Non traditional military responses to complex
emergencies, Decision Support Department, Center for Naval Warfare Studies, U.S. Naval War
College, Newport, RI, USA, DSD Research Report 97–1.
Heckerman, David, Abe Mamdani & Michael P. Wellman (1995). Real-world applications of Bayesian
networks. Communications of the ACM, 38(3):24-26.
Henrion, Max (1987). Uncertainty in Artificial Intelligence: Is Probability Epistemologically andHeuristically Adequate? In: Mumpower J.L., Renn O., Phillips L.D., Uppuluri V.R.R. (eds) Expert
Judgment and Expert Systems. NATO ASI Series (Series F: Computer and Systems Sciences), vol 35.
Springer, Berlin, Heidelberg
Henrion, Max (1989). Some practical issues in constructing belief networks. Kanal, L.N., Levitt, T.S. &Lemmer, J.F. (eds.), Uncertainty in Artificial Intelligence 3. Elsevier Science Publishers B.V. (North
Holland), pages 161-173.
Henrion, Max (1990). An introduction to algorithms for inference in belief nets. In Henrion, M.,
Shachter, R.D., Kanal, L.N. & Lemmer, J.F. (eds.), Uncertainty in Artificial Intelligence 5, Elsevier
Science Publishers B.V. (North Holland), pages 129-138.
Henrion, M., John S. Breese & Eric J. Horvitz (1991). Decision analysis and expert systems. AI
Magazine, 12(4):64-91.
Horvitz, Eric J., John S. Breese & Max Henrion (1988). Decision theory in expert systems andartificial intelligence. International Journal of Approximate Reasoning. 2(3):247-302.
Hulst, Joris (2006). Modeling physiological processes with dynamic Bayesian networks. M.Sc. thesis,
Delft University of Technology, Delft, The Netherlands.
Jensen, Finn V. (1996). An Introduction to Bayesian Networks. Springer Verlag, New York.
Jensen, Finn V., Steffen L. Lauritzen & Kristian G. Olsen (1990). Bayesian updating in recursive
graphical models by local computations. Computational Statisticals Quarterly, 4:269-282.

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Resources
Kernighan, Brian W. & Dennis M. Ritchie (1988). The C Programming Language. Prentice Hall PTR,
2nd edition.
Lauritzen, Steffen L. & David J. Spiegelhalter (1988). Local computations with probabilities ongraphical structures and their application to expert systems (with discussion). Journal of the Royal
Statistical Society, Series B (Methological), 50(2):157-224.
Lin, Yan & Marek J. Druzdzel (1997). Computational advantages of relevance reasoning in Bayesian
belief networks. In Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence(UAI-97), Morgan Kaufmann: San Mateo, CA, pages 342-350.
Paul P. Maaskant (2006). A causal model for qualitative reasoning. M.Sc. thesis, Man-machineinteraction group, Delft University of Technology (available at
http://www.kbs.twi.tudelft.nl/Publications/MSc/2006-PaulMaaskant-Msc.html).
Paul P. Maaskant and Marek J. Druzdzel (2008). An ICI Model for opposing influences. In
Proceedings of the Fourth European Workshop on Probabilistic Graphical Models (PGM-08), Manfred
Jaeger & Thomas D. Nielsen (eds.), Hirtshals, Denmark, pages 185-192.
Matzkevich, Izhar & Bruce Abramson (1995). Decision analytic networks in artificial intelligence.
Management Science, 41(1):1-22.
Morgan, M. Granger & Max Henrion (1990). Uncertainty: A Guide to Dealing with Uncertainty in
Quantitative Risk and Policy Analysis. Cambridge University Press, Cambridge.
Murphy, Kevin P. (2002). Dynamic Bayesian Networks: Representation, Inference and Learning.
Doctoral dissertation, University of California, Berkeley.
Neapolitan, Richard E. (1990). Probabilistic Reasoning in Expert Systems: Theory and Algorithms.
John Wiley & Sons, New York.
Pearl, Judea (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.
Morgan Kaufmann Publishers, Inc., San Mateo, CA.
Aníbal Pérez-Liñán (2008). “El dilema estrategico de la seguridad ciudadana y el Estado democráicode derecho.” In Alexandra Steinmetz (ed.), Informe Estado de la Región. State of the Nation—Region
Program, Pavas, Costa Rica, http://www.estadonacion.or.cr/ Chapter 12, pages 460–524.
Quinn, N.R., Jr & M.A. Breuer (1979). A force directed component placement procedure for printed
circuit boards. IEEE Transactions on Circuits and Systems, CAS 26, pages 377–388.
Savage, Leonard (1954). The Foundations of Statistics. Dover, New York.
Simon, Herbert A. (1996). The Sciences of the Artificial. 3rd edition. MIT Press.

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Resources
Spiegelhalter, David J., A. Philip Dawid, Steffen L. Lauritzen & Robert G. Cowell (1993). Bayesiananalysis in expert systems. Statistical Science, 8(3):219-283.
Whittaker, Joe (1990). Graphical Models in Applied Multivariate Statistics. John Wiley & Sons,
Chichester.
Yuan, Changhe & Marek J. Druzdzel (2003). An Importance Sampling Algorithm Based on EvidencePre-propagation. In Proceedings of the Nineteenth Conference on Uncertainty in Artificial
Intelligence (UAI-03), Morgan Kaufmann: San Mateo, CA, pages 624-631.

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Index

-A-

-D-

about GeNIe 34
acknowledgments 43
acknowledment 42
acyclic directed graph 47
annotation 110
Apple 39
applications 168
approximate belief updatingarc 106, 182
arc thickness 182

DBN 208, 209, 215
decision analysis 46
define properties 10
del Favero 50
DeMorgan gate 80
DeMorgan node 79
disclaimer 42
50-Eedge 106
effects of changes 50
effects of changes in structure 50
enabling most effective actions calculationentering evidence 172
EPIS 164
EPIS Sampling 164
error messages 77
evidence 172
example networks 37-BBayesian approach 47
Bayesian belief network 47
Bayesian inference 50
Bayesian network 47
Bayesian probability 46
Bayesian updating 50
belief network 47
belief updating 50
books 230
bug reporting 42
building a qualitative Bayesian networkbuilding blocks of QGeNIe 54-F10-CCanonical Nodes 80, 95
case manager 199
cases 199
CAST gate 95
CAST node 79
causal probabilistic network 47
Chang 50changes in structure 50
clustering algorithm 164
conferences 231
console 77
controling values 175
Controlled status icon 107
copyright notice 42
create a node 10
creating a qualitative Bayesian network

QGeNIe Modeler203

Facebook 232
File Menu 155
Focus status icon 107
Format Toolbar 134
frequentist interpretation
full-screen mode 132
Fung 50

46-Ggeneral tab 121
graph layout 129, 138
Graph View 57-HHardware and software requirements
Help Menu 78
Henrion 50

37

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Index
Node Property Sheets 121
node status icons 107
node type 79-Iimpact of observing values 50
Implied status icon 107
inference 215
inference algorithms 160
introduction 168
iOS 39-Jjoint-tree algorithm-Kkeyboard shortcuts 164
knowledge engineering 209

parent ordering 129, 138
Peot 50
probability 46
propensity view 46

-Q-

-L-

QDSL file format 160
QGeNIe version 37
QGeNIe workspace 54
qualitative Bayesian network 170, 172, 175, 179, 182
qualitative Bayesian networks tutorial 10
qualitative dynamic Bayesian network 208, 209, 215

Layout Menu 129, 138
LinkedIn 232
literature list 232
loading models 148-MMac OS 39
MacIntosh 39
manipulable 202
manipulation 50, 175
marginal probability distribution 179
Menu Bar 56
model repository 231
models 231
most effective actions calculation 202, 205
movies 232-N-

QGeNIe Modeler

objectivist interpretation 46
observable 202
Observed status icon 107
observing 50
Output Window 77

-P-

164

navigation 145
Network Menu 162
Network Property Sheets
Node Menu 161
node properties 202

-O-

112

-Rread me first 8
references 232
relevance-based decomposition
research papers 230
resources 230, 231, 232
retracting evidence 172

164-Ssave model 10saving models 148
selection 140
Shachter 50
size 132
SMILE 35
spring embedder 129, 138

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Index
Standard Toolbar 119
start here 8
Status Bar 76
stochastic sampling 50
strength of influence 182
subjectivist interpretation 46
subjectivist view 46
submodel 97
submodel node 79
Submodel Property Sheets 117-Ttext box 109
Tools Menu 119transparent mode
Tree View 72
Twitter 232

121-Uuser properties 121
using QGeNIe 168-Vvalue tab 121
view results 10
viewing results 179-Wwarnings

77-YYouTube

232-Zzooming

132

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