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.

See Examples & Learn More

• Chapter 4: Knowledge Modeling & Probabilistic Reasoning

• Webinar: Reasoning About Renewable Energy

• Webinar: Optimizing Health Policies

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.

See Examples & Learn More

• Bayesia Expert Knowledge Elicitation Environment (BEKEE)

• Seminar: Knowledge Elicitation & Geopolitical Reasoning Under Extreme Uncertainty with Bayesian Networks

• Webinar: Differential Diagnosis of COVID-19

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.