Uniqueness of the Level Two Bayesian Network Representing a Probability Distribution

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 ð ¼ of random variables, this paper presents a result on the computation of the probability distribution of a subset 𠑆 of ð ¼ using separately a computation algorithm and D-separation properties. It also shows the uniqueness of the obtained result.