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