Belief Networks and Decision Networks
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.
Norsys bayesian belief software, based in Vancouver, Canada.