Belief networks (also known as Bayesian networks, Bayes networks and causal probabilistic networks), provide a method to represent relationships between propositions or variables, even if the relationships involve uncertainty, unpredictability or imprecision. They may be learned automatically from data files, created by an expert, or developed by a combination of the two. They capture knowledge in a modular form that can be transported from one situation to another; it is a form people can understand, and which allows a clear visualization of the relationships involved.

By adding decision variables (things that can be controlled), and utility variables (things we want to optimize) to the relationships of a belief network, a decision network (also known as an influence diagram) is formed. This can be used to find optimal decisions, control systems, or plans.
For examples of a variety of Bayes nets, explore our BN library.

Norsys specializes in making advanced belief network and decision network technology practical and affordable.

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