## Materials and Methods  
### General Method  
The scenario considered here is that the biomechanics expert has been asked to opine about whether the defendant failed to meet a generally accepted standard, which may come from a government regulator, voluntary standards organization, or other source. Suppose the standard is stated in terms of acceptable and unacceptable ranges of some quantity that is expressed as a real number. The standard may be that the quantity is above some threshold, and sometimes it must be below a threshold. The proposed method for developing a Bayesian network to assist in developing the opinion has six steps:  
1. **Identify the deterministic model based on principles of engineering mechanics**. This step is simply constructing a deterministic mathematical engineering mechanics model of the system using established methods from biomechanics [3]. The model should be one that can be represented with algebraic equations or inequalities.
2. **Represent model as a directed acyclic graph**. Nodes represent variables. Directed edges encode the algebraic relationships between variables. The result should be a directed acyclic graph. If it cannot be constructed as a directed acyclic graph, the method fails; if it can be, proceed to step three.
3. **Identify nodes that can be modeled as random variables**. There are three obvious sources of uncertainty suitable for inclusion in the model: (1) variability in anthropometric parameters, (2) variability in human performance, and (3) measurement error of model inputs. People come in many shapes and sizes, and statistical methods are commonly used to model anthropometric variation. How a person moves, which drives the kinematic and kinetic inputs to biomechanical models, can be highly variable due to a variety of reasons including noise in the motor control system. Finally, empirical measurements have error and statistical methods are well established for representing the error using probability distributions. Distributions must be selected and parameters specified.
4. **Extend the directed acyclic graph to a full Bayesian network**. Identify all leaf nodes in the directed acyclic graph that have an outdegree (number of edges directed out of a node) of one. For all of the nodes identified in this step that correspond to variables in step two, apply the corresponding probability distributions. At this point, the Bayesian network is a stochastic implementation of a traditional biomechanical model. Note that, at this point, model inputs are limited to traditional measurements made in biomechanics, and these do not include medical data available in civil litigation case files.
5. **Identify outcomes (events) that have occurred in the legal case of interest that are known in hindsight**. Civil injury litigation often arises because someone has been injured. Add nodes and edges that model the relationship between variables already in the model and the injury event.  
Add node for the probability that a generally accepted standard was exceededThis is the node that will be used to address the \"more likely than not\" interpretation of \"reasonable scientific certainty\" put forward in _[PERSON] v. Town of Walpole_. For the node added in step five, add a node representing a Boolean variable. Add an edge from the existing node to the new node that does not transform the variable at all; it merely makes the variable available to the new node. Add a node probability table to the new node, such that the Boolean variable takes on a value of true when the variable associated with the incident edge is greater than--or less than, depending on the context--the generally accepted standard for this variable (the direction should be selected so that the variable takes on a value of true if the standard is not met).