Knowledge Modeling
Knowledge Modeling
Subject matter experts often express their causal understanding of a domain in the form of diagrams in which arrows indicate causal directions.
This visual representation of causes and effects has a direct analog in the network graph in BayesiaLab.
Nodes (representing variables) can be added and positioned on BayesiaLab’s Graph Panel with a mouse click, and arcs (representing relationships) can be “drawn” between nodes.
The causal direction can be encoded by orienting the arcs from cause to effect.
The quantitative nature of relationships between variables, plus many other attributes, can be managed in BayesiaLab’s Node Editor.
In this way, BayesiaLab facilitates the straightforward encoding of one’s understanding of a domain.
Simultaneously, BayesiaLab enforces internal consistency so that impossible conditions cannot be encoded accidentally.
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Knowledge Elicitation
In addition to directly encoding explicit knowledge in BayesiaLab, the Bayesia Expert Knowledge Elicitation Environment (BEKEE) is available to acquire the probabilities of a network from a group of experts.
The Bayesia Expert Knowledge Elicitation Environment (BEKEE) is a web service that allows you to systematically elicit both explicit and tacit knowledge from multiple expert stakeholders.
Discrete, Nonlinear, and Nonparametric Modeling
BayesiaLab contains all “parameters” describing probabilistic relationships between variables in Conditional Probability Tables (CPT), meaning no functional forms are utilized.
Given this nonparametric, discrete approach, BayesiaLab can conveniently handle nonlinear relationships between variables. However, this CPT-based representation requires a preparation step for dealing with continuous variables, namely discretization. This consists of manually or automatically defining a discrete representation of all continuous values.
BayesiaLab offers several tools for discretization, which are accessible in the Data Import Wizard, in the Node Editor, and in a standalone Discretization function. Univariate, bivariate, and multivariate discretization algorithms are available in this context.
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