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BayesiaLab is a comprehensive tool for creating and utilizing Bayesian networks. BayesiaLab allows defining, learning, modifying and analyzing Bayesian network models.

This user's guide describes the functionalities and the user interface of BayesiaLab. 

Info
titleBayesiaLab's Help Functions
When BayesiaLab is running, you can always press  to bring up the help files. Alternatively, pressing  will display the contextual help cursor. Once it's active, you can click on any component, including menus and submenus, to display the context-specific help pages.

Introduction

Bayesian networks are graphical structures, consisting of nodes and arcs. Nodes represent random variables, arcs represent direct probabilistic relationships between the connected nodes/variables. These probabilistic relationships are quantified by probability distributions. Such probability distributions are recorded in conditional probability table that are associated with each node.

Bayesian networks can be machine-learned from data or, alternatively, they can be manually modeled by domain experts. Once a Bayesian network is created, it can be used for updating the probability distribution of each variable, given any evidence set on other variables in the network