- 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.
- The causal direction can be encoded by orienting the arcs from cause to effect.
- 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.
- In addition to directly encoding explicit knowledge in BayesiaLab, the Bayesia Expert Knowledge Elicitation Environment (BEKEE) is available for acquiring the probabilities of a network from a group of experts.
- 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.