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
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,
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.
To try it for free: download the latest version, leave the password dialog box empty and click 'Limited Mode'.