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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, arcs (representing relationships) can be “drawn” between nodes. The causal direction can be encoded by orienting the arcs from cause to effect

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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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threat_assessmentIn 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. 

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