Talk:Bayesian network

message: Wasn't there some big history to bayesian networks?  They were one of the first statisical processing methods invented? no!
The article as it stands (2003/12/26) limits the definition unnecessarily. I'm going to edit the article to address these points:
(1) a node can represent any kind of variable, not just discrete random variables; variables need not be discrete, and they need not be random.
(2) the arcs don't represent correlation; correlation in probability theory has a certain well-defined meaning which is not applicable here. What arcs do represent is conditional dependence.
(3) "Conditional probability table" assumes that the variables involved are discrete; need to allow for continuous variables.
(4) The list of applications can be expanded.
----
I've addressed (or tried to) items (1) through (3) above. Wile E. Heresiarch 07:41, 27 Dec 2003 (UTC)
thread_title:  history 

message: No doubt it's a wonderfully accurate and concise explanation exactly what a Bayesian network is but it's useless to those of us who aren't striving for that PhD in math. At the very least it needs an introduction stating, in plain english, what such a network is. I can't be the one to write that introduction because after reading the article I have less of a clue about what they are then before I read it. The sort of detail in this article is great as a stand-alone webpage or reference, but it's not an encyclopedia entry. Frustrating.
:I added a small hopefully more human readable 3 sentences to the introduction... --UmassThrower 04:59, 23 March 2007 (UTC)
replies: text: It might, at the least, be helpful to add some links/references to what the various pieces of mathematical notation mean/refer-to.  I have a CompSci/Math degree, but I'm still scratching my head here trying to figure out what the formulae actually mean. -- 91.123.228.33 (talk) 12:17, 21 May 2012 (UTC)
replies: text: I think an overview of the practical applications in the lead paragraph would help out a lot. Currently the lead section gives very little context on the subject (i.e. what are bayesian networks and why are they important?) WP:LEAD and Wikipedia:Make_technical_articles_accessible#Articles_that_are_unavoidably_technical have some pointers on this. It might also be interesting to mention the origin in the lead section. Bayes' theorem is given only passing mention in the article currently, maybe more info on this would be useful. Aside from the lead section, linking jargon words throughout the article could help readers in understanding the material.--Eloil 19:58, 17 April 2007 (UTC)
replies: text: Completely agree !! This type of technical level is ONLY enjoyed by experts, not the general public, who are no wiser !  —Preceding unsigned comment added by 202.53.236.171 (talk) 04:56, 25 April 2008 (UTC)
replies: text: Also agree, it should be little be more friendly to others  —Preceding unsigned comment added by Dsundquist (talk • contribs) 18:16, 20 December 2008 (UTC)
thread_title:  Way, way, wayyyyy too technical 

message: do we have a reference for this?  It directly contradicts Pearl.
"A Bayesian network need not to represent causal relationships. However, if knowledge about causality is available it is natural to use it for selecting the parent variables when building the network, thus resulting in causal Bayesian networks."
MisterSheik 21:18, 27 March 2007 (UTC)
replies: text: In addition to Pearl's causal model Bayesian networks can be interpreted as (non-causal) models of probabilistic relationships. See Heckerman: A Bayesian approach to learning causal networks for a discussion of the two interpretations. AnAj 17:47, 29 March 2007 (UTC)
thread_title: reference?

message: I can see from the recent history that an edit war may be looming.  In defence of my recent changes to the definition, I want to reaffirm on the talk page that links don't represent statistical independences, but direct causal relationships.  Two nodes can be dependent without being connected (for example, if they are results of the same cause.)  These nodes become independent once that causal variable is known.  Similarly, they are made dependent by an effect that becomes known (or if its effect is know, and so on). This is what should be explained under d-separation, which really shouldn't have its own article, but should be explained here.
d-separation is the whole point of Bayesian networks--not efficient computational properties.  (Perhaps the computations are easier for students to grasp and so presented first by instructors?)
Tractability is also not an important point because the conditional probability of a variable on its parents could be intractable.
MisterSheik 21:54, 27 March 2007 (UTC)
thread_title: looming edit war?

message: * In the Definition section, are "N" and "n" used interchangeably?--Skoch3 16:51, 29 March 2007 (UTC)
replies: text: Yes. It's fixed now. AnAj 17:58, 29 March 2007 (UTC)
thread_title: N or n in "Definition" section?

message: I think the article is getting better, but I still think we need to cut it right in two.  The definition makes it sound like there the causal thing is some funky requirement that you can drop.  The reality is that the causal version is completely different: the operations that you can perform on it work differently.  For example, checking to see if two nodes are independent.  In the Pearl's "version", d-separation (a path, with specific requirements--this is the magic of causal relationships) is the procedure that determines independence, but without the notion of causality, you're back to m-separation (any path).  The belief updating (what's been labeled inference) is done with likelihoods and priors in pearls version; i'm not sure how it's done otherwise.  Also, the notion of rewiring the network-- in fact, the whole meaning of network has changed.  The introduction should really reflect the reality that there are in fact two separate concepts.  They are as different as markov networks and bayesian network, pearl's bayesian networks and the other "directed graphical models".  I'm okay with including them in the same article since that reflects the reality of how people use the term, but I think they should be presented in a more side-by-side fashion. MisterSheik 21:11, 1 April 2007 (UTC)
thread_title: Good start AnAj

message: This new version (with two definitions of a BN) is clearly incorrect. I have Causality by Pearl lying in front of me: Pearl clearly distinguishes between Bayesian Networks and Causal Bayesian Networks. Also, in the current version d-separation only seems to apply to Causal BNs, which is clearly wrong. In short, I think we should revert to one of the earlier versions. Tomixdf 06:15, 17 April 2007 (UTC)
thread_title: Terrible

message: Things that should/could be added:
* Observational equivalence, d-sep and v-structure + plus a simple example of what it all means
* Some more information on Causal BNs, or even a seperate page
* Inference using Gibbs sampling
* Link with factor graphs
* Variational methods, free energy interpretation
Tomixdf 22:09, 22 April 2007 (UTC)
thread_title: To do

message: On 13:01, 17 May 2007 Tomixdf (Talk | contribs) (16,635 bytes) (Undid revision 131546816 by Linas (talk) In a BN, edges are NOT state transitions. This section is thus confusing.)
performed a revert of the following:
:Related concepts
:A related concept is that of the topological finite state machine, of which the quantum finite state machine is a special case. Here, nodes represent internal states of the machine, while arrows are interpreted as functions from one topological space to another. The resemblance is closest when the topological spaces are probability spaces or measure spaces, in which case the input string to the automaton corresponds to a specific path through a Bayesian network.
This paragraph never said that the edges of a BN are state transitions. I am not sure why you found this confusing; perhaps some additional word-smithing is in order? (This was meant to be a quick-n-dirty remark, not an exposition). linas 01:08, 18 May 2007 (UTC)
replies: text: In the topological finite state machine you describe, the nodes seem to be states and the edges denote state changes. This would be similar to an HMM state diagram, but not to a BN diagram, in which the nodes are variables and the edges dependencies. I suspect you are interpreting the BN diagram as an HMM state diagram, and as a result this section would be better suited for the HMM page. Tomixdf 07:56, 18 May 2007 (UTC)
replies: text: By "HMM" I assume you mean hidden Markov model. The article already asserts broad equivalence to Markov models and to finite subshifts; the section I wanted to add was not titled "equivalent concepts", it was titled "related concepts". Point taken, the whole suite of articles in this area is in poor condition. Perhaps I'll spend some time here. linas 14:50, 18 May 2007 (UTC)

text: The core observation is that one wants to integrate over all possible state transitions, or integrate over the cylinder sets of the system. What you get after the integration is a Bayesian network; the various conditional probabilities are the measures of the cylinder sets. If the measures are translation invariant, you get things like the Viterbi algorithm (an example of a dynamical Bayesian network); but they need not be translation invariant. linas 15:00, 18 May 2007 (UTC)
thread_title:  Relationship to automata 

message: i'm looking for ways to validate bayesian networks but can't find it here.. shouldn't there be a section about it?  —Preceding unsigned comment added by 137.120.76.211 (talk) 10:55, 21 January 2008 (UTC)
thread_title: Validation methods?

message: I think the points in the Intro bullet list are important to the full understanding of Bayes networks. But dealing with the subjectivity of the paradigm shouldn't be up in the intro. The concept is important. I agree with it. I'm writing a thesis about it. But its confusing to neophytes when placed in the fourth sentence. Also note this article is flagged as too technical. So I put it in History since it mentions history.
IMHO Some of these points should go up in the end of definitions and concepts. Further, it is unfortunate that History is way down at the bottom and very short. But the casual venue of a wiki article suggests intro first, examples second, formal definitions third, elaborations, histories, and controversies (like frequentism/belief debates, Dempster Shafer, limits, boundary conditions, causality vs inference, ...) fourth. Of course, feel free to revert my change. But I still think important elaborations should go among the elaborations. It should probably be first among those!
--ClickStudent (talk) 19:32, 25 May 2008 (UTC)
thread_title: Moved Bullet List from Intro to History Section

message: As a not-good mathematician I feel a bit more explanation of how the conditional probabilities in the Rain and Sprinkler tables lead on to the probabilities in the Sprinkler/Rain/Wet Grass table.
Badmather (talk) 16:46, 22 July 2008 (UTC)
replies: text: It is NOT cyclical, because it is directed. If you start from a node and follow directed edges, u will never come back to the same node again. The rain causes the sprinkler to be turned on less frequently, than the absence of the rain. See the second graph with conditional probability tables (CPT). The graph structure only shows that there is a causal relationship between the nodes. The CPT explains that relationship. Hous21 (talk) 13:52, 29 November 2017 (UTC)
thread_title:  The 'simple Bayesian network' figure for Rain/Sprinkler/Grass 

message: As a not-good mathematician I would like to request a bit more detail on how the numbers in the 'Sprinkler/Rain/Grass Wet' truth table are derived from the 'Rain' and 'Sprinkler' truth tables.
Badmather (talk) 08:19, 23 July 2008 (UTC)
thread_title:  Request for detail on 'A simple Bayesian network' example 

message: I removed the following, and left a link to a more complete list.
:Free and open source software (alphabetical)
* Ace, the Bayesian network compiler: http://reasoning.cs.ucla.edu/ace (Works with SamIam, creator of BANSY3)
* AISpace, applet for belief and decision networks with explanation facilities designed for teaching and learning: http://aispace.org/bayes
* BANJO: Bayesian Network Inference with Java Objects: http://www.cs.duke.edu/~amink/software/banjo
* BANSY3 - Freeware. From the Non Linear Dynamics Laboratory. Mathematics Department, Science School, UNAM.
* BN4R: http://bn4r.rubyforge.org/
* BNT: Kevin Murphy's Bayesian Network Toolbox for MatLab: http://bnt.sourceforge.net/
* dlib C++ Library: http://dclib.sourceforge.net/
* GeNIe & SMILE: http://genie.sis.pitt.edu
* JavaBayes, Bayesian Networks in Java: http://www.pmr.poli.usp.br/ltd/Software/javabayes/
* OpenBayes: http://www.openbayes.org
* pebl: Python Environment for Bayesian Learning: http://pebl-project.googlecode.com
* ProBT-academic a free version for the academic community of the ProBAYES' industrial package
* RISO: http://sourceforge.net/projects/riso/ (distributed belief networks)
* SamIam: http://reasoning.cs.ucla.edu/samiam  (Works with Ace, above)
* UnBBayes: BN, ID, Multiply Sectioned Bayesian Network (MSBN) and Multi-Entity Bayesian Networks (MEBN). It also includes various algorithms for Bayesian Learning. From the Group of Artificial Intelligence at University of Brasília (UnB), Brazil.
:Commercial software (alphabetical)
* AgenaRisk Bayesian network tool: http://www.agenarisk.com
* BayesBuilder: http://www.snn.ru.nl/nijmegen/index.php3?page=31
* Bayesia: http://www.bayesia.com
* Bayesian network application library: http://www.norsys.com/netlibrary/index.htm
* BNet: http://www.cra.com/bnet
* Causeway: http://www.inet.saic.com/
* Dezide: http://www.dezide.com
* dVelox: http://aparasw.com/dVelox
* Hugin: http://www.hugin.com
* MSBNx: a component-centric toolkit for modeling and inference with Bayesian Network (from Microsoft Research): http://research.microsoft.com/adapt/MSBNx/
* Netica: http://www.norsys.com
* Promedas (Bayesian medical decision support): http://www.promedas.nl
* ProBayes: http://www.probayes.com
* Quiddity: Esp. for large models.
--Adoniscik(t, c) 04:15, 11 September 2008 (UTC)
thread_title:  Linkfarm 

message: I replaced this:
:Nodes can represent any kind of variable, be it a measured parameter, a latent variable or a hypothesis. They are not restricted to representing random variables, which represents another "Bayesian" aspect of a Bayesian network.
with this:
:Nodes represent random variables, but in the Bayesian sense: they may be observable quantities, latent variables, unknown parameters or hypotheses.
The nodes must be random variables in the sense that they must have a probability distribution. They are Bayesian in the sense that latent variables, parameters etc. are random variables. -3mta3 (talk) 18:06, 27 April 2009 (UTC)
thread_title:  Random variables 

message: I think a cleanup needs to be performed of the references and software. We don't need to refer to every paper or book written on the topic. Likewise, a previous commenter had actually removed the list of software, but these have since been added back in.
Any nominations of what should be kept/removed? Should we make a List of Bayesian network software? —3mta3 (talk) 10:29, 2 May 2009 (UTC)
replies: text: Actually, I would like to see a Comparison of Bayesian network software, but I wouldn't know where to start. Maghnus (talk) 04:53, 21 July 2010 (UTC)
thread_title:  Cleanup 

message: In accordance with WP:JARGON: each of the definitions (Bayesian network#Factorization definition, Bayesian network#Local Markov Property, etc) requires a second paragraph that explains the meaning of the symbols used, in a way that will make sense to (at least) computer science majors with a poor grasp of probability notation but a strong desire for precision. This article is linked to from the introductory article on artificial intelligence and should make sense to a student taking a course in AI. Ideally, it should make sense to an intelligent and patient journalist researching modern AI, who has no background in mathematical notation at all. As it is, it only makes sense to someone who is likely to know this definition already. ---- CharlesGillingham (talk) 17:59, 15 September 2009 (UTC)
replies: text: Yes lacking definitions: guessing at what "For all the following, let G = (V,E) be a directed acyclic graph (or DAG)," means I would have said "For all the following, let G be a directed acyclic graph (or DAG), V a vertice(node)??? and E an edge linking two vertices. Then  G = (V,E) ..., but really this should be in set notation - if in fact this is what that sentence means.  —Preceding unsigned comment added by Thomas Kist (talk • contribs) 20:59, 30 September 2009 (UTC)

text: Agree.  But it is not just the jargon, it is the way it is way maths articles are written Wikipedia generally.  Very formal and abstract, with little material to build intuitions.  I made other notes below..Tuntable (talk) 04:31, 27 December 2012 (UTC)
thread_title:  Layman's terms required 

message: Notable Projects incorporating Bayesian networks:
*Medical Diagnosis
**CPSP- liver and bile disease
**Pathfinder- Heckerman- lymph-node diseases
**qmr-dt
**munin  — Preceding unsigned comment added by Osnetwork (talk • contribs) 10:18, 24 March 2012 (UTC)
thread_title:  applications - should add list of notable projects using bn 

message: This seems wrong: Edges represent conditional dependencies; nodes which are not connected represent variables which are conditionally independent of each other.  The rules of d-separation and independence are more complicated than that. ★NealMcB★ (talk) 05:37, 14 November 2012 (UTC)
thread_title:  circumstances for conditional independence 

message: Like most maths articles on Wikipedia, this one is unintelligible to someone that does not already know the material.
Consider an intelligent programmer with a basic knowledge of maths who wants to get an ideal of what all the talk about Baysian Networks is about, and whether they should read more.  Well, they won't be any the wiser from this article.  I have a PhD in Comp Sci (but limited knowledge of this field) and I can only follow the sprinkler example with effort, so don't tell me that it is intelligible.
The topic is technical, and we certainly do not want journalistic waffle.  But an introductory example of showing how the joint probablilities for a simple two node example can be simplified would be a good start.  Then maybe somthing about independent nodes which is not even mentioned.  Etc. 
The goal of Wikipedia is not to demonstrate how clever Wikipedians are.Tuntable (talk) 04:27, 27 December 2012 (UTC)
thread_title:  Unintelligible to non-experts 

message: The formulas in the example are way way too long, they even cause considerable horizontal scrolling. 
We should leave out the complete derivation (which is pointless imho) or find another example / notation.  — Preceding unsigned comment added by 143.177.88.196 (talk) 18:49, 22 February 2014 (UTC)
thread_title:  Example formulas 

message: 'Hierarchical Bayesian Model' leads here. While it's true that a HBM is a special case of a Bayesian Network, it is suffiently widely used that it should not be subsumed under the more general heading. Linear regression is a special case of the linear model, which in turn is a special case of the generalised linear model and we have Wikipedia articles for all three. I think we need a separate article for the Hierarchical Bayesian Model.
Blaise (talk) 08:05, 21 March 2014 (UTC)
thread_title:  Hierarchical Bayesian Model 

message: Hello fellow Wikipedians,
I have just modified  on Bayesian network. Please take a moment to review my edit. If you have any questions, or need the bot to ignore the links, or the page altogether, please visit this simple FaQ for additional information. I made the following changes:
*Added archive https://web.archive.org/web/20110723084249/http://www-fis.iarc.fr/~martyn/software/jags/ to http://www-fis.iarc.fr/~martyn/software/jags/
*Added archive https://web.archive.org/web/20070927153751/https://www.dcs.qmul.ac.uk/~norman/papers/Combining%20evidence%20in%20risk%20analysis%20using%20BNs.pdf to https://www.dcs.qmul.ac.uk/~norman/papers/Combining%20evidence%20in%20risk%20analysis%20using%20BNs.pdf
*Added archive https://web.archive.org/web/20060719171558/http://research.microsoft.com:80/research/pubs/view.aspx?msr_tr_id=MSR-TR-95-06 to http://research.microsoft.com/research/pubs/view.aspx?msr_tr_id=MSR-TR-95-06
*Added archive https://web.archive.org/web/20060719171558/http://research.microsoft.com:80/research/pubs/view.aspx?msr_tr_id=MSR-TR-95-06 to http://research.microsoft.com/research/pubs/view.aspx?msr_tr_id=MSR-TR-95-06
*Added archive https://web.archive.org/web/20090923200511/http://wiki.syncleus.com:80/index.php/DANN:Bayesian_Network to http://wiki.syncleus.com/index.php/DANN:Bayesian_Network
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thread_title:  External links modified 

message: By my latest count this pointless listcruft contained 26 (!) examples and mentions with zero relevant context and additional topic-related details. Aside from being pointless and indiscriminate, this list was also misused to cite spam various journal articles by a relatively new author in this area. Information about noteworthy applications from established experts could be added, but such coverage should contain additional relevant context beyond a trivial list. GermanJoe (talk) 21:07, 6 August 2018 (UTC)
thread_title:  Indiscriminate list of "Applications" - removed 

message: The page Bayesian neural network currently redirects here. From what I can tell, the networks discussed here in regards to Bayesian networks have little to do with neural networks. I'm none too familiar with Bayesian neural networks (hence looking it up on Wikipedia), but are Bayesian neural networks a variety of Bayesian networks (in which case it might be worth mentioning them explicitly in this article), or is the similarity in names coincidental (in which case it might be worth un-redirecting the Bayesian neural network article, and/or expanding on that distinction in-text)? -- 22:57, 31 January 2019 (UTC)
thread_title:  Distinction between "Bayesian network" and "Bayesian neural network"? 

message: I would like to propose adding a link to an interactive Bayesian network model repository created and maintained as a public service by BayesFusion, LLC.  I am a professor at the University of Pittsburgh but also affiliated with BayesFusion, so I would rather not edit the page directly.  The repository contains almost 100 models from various sources (acknowledged in case of each model) that can be examined interactively using any modern web browser.  Visitors to that page can enter evidence and view results of computation.  Here is the repository address:
https://repo.bayesfusion.com/ — Preceding unsigned comment added by Druzdzel (talk • contribs) 09:35, 3 June 2019 (UTC)
replies: text: I object. This seems to basically be a software demo, and Wikipedia isn't for promotion of BayesFusion's products. - 10:51, 17 June 2019 (UTC)
replies: text: I strongly object to this objection.  BayesFusion, LLC, is indeed a commercial enterprize.  However, the repository has been created as a public service to the Bayesian network community.  It has been announced recently as such on the UAI mailing list (the prime platform for communicating news and results in the area of Bayesian networks).  All model come from Bayesian network literature and are acknowledged in the screen comments.  Visitors to that page can observe nodes and see results calculated by the Bayesian network models, something that the Wikipedia page does not come close to providing.  This is a valuable public service.  Please ask around in the community rather than accusing of promotion.  — Preceding unsigned comment added by Druzdzel (talk • contribs) 11:27, 17 June 2019 (UTC)
thread_title:  Bayesian network public interactive model repository 

message: Descendant is not defined. Children only, or children + grandchildren + ...? Sigma^2 (talk) 19:38, 12 September 2021 (UTC)
thread_title:  Local Markov property 

message: The example {Wet Grass implies Sprinkler (non-exclusive) or Rain} is wrong. Wet grass implies a set of possible causes and even if we broaden "Rain" to mean air-borne condensation followed by precipitation of water, and "Sprinkler" to mean any application of water (wet = surface wetted with water) by artificial means, that still leave several other possibilities including direct condensation of water on the grass (dew) and flooding to name two examples. In some areas, I'd guess, dew is by far the most common (hence intuitive) way grass becomes wet. 98.21.213.85 (talk) 12:30, 20 December 2023 (UTC)
thread_title:  Example