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, 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.
- QuickStart Guide on Knowledge Modeling
- Chapter 4 in Bayesian Networks and BayesiaLab
- Webinar: Probabilistic Reasoning Under Uncertainty
In addition to having individuals directly encode their 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. BEKEE offers a web-based interface for systematically eliciting explicit and tacit knowledge from multiple stakeholders.
- Bayesia Expert Knowledge Elicitation Environment (BEKEE)
- Seminar: Knowledge Elicitation & Reasoning with Bayesian Networks
- Webinar: Bayesian Networks for Risk Management Without Data