New York Institute of Technology, College of Arts and Sciences, P.O. Box 840878, Amman 11184, Jordan
Copyright © 2011 Linda Smail. This is an open access article distributed under the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Bayesian Networks are graphic probabilistic models through
which we can acquire, capitalize on, and exploit knowledge. they are becoming
an important tool for research and applications in artificial intelligence
and many other fields in the last decade. This paper presents
Bayesian networks and discusses the inference problem in such models. It
proposes a statement of the problem and the proposed method to compute
probability distributions. It also uses D-separation for simplifying
the computation of probabilities in Bayesian networks. Given a Bayesian
network over a family of random variables, this paper presents a result
on the computation of the probability distribution of a subset of
using separately a computation algorithm and D-separation properties.
It also shows the uniqueness of the obtained result.