Knowledge Modeling

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 (opens in a new tab) 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

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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