Tractable Problems in Bayesian Networks

The complexity of probabilistic reasoning with Bayesian networks has recently been proven to be NP complete So reducing the complexity of such networks is an active issue in uncer tainty reasoning Much work on such as compressing the probabilistic information for Bayesian networks and optimal approximation algorithm for Bayesian inference has been suggested In this paper we study a class of tractable problems in Bayesian networks aiming at reducing their computing complexity from non polynomial amount to polynomial one The main challenge for tractable problems in Bayesian networks is the propagation of probabilities To solve this prob lem we construct a new model to integrate statistical technique into Bayesian networks using an encoding technique For e ectiveness an optimization for conditional probability matrix is also built We evaluated the proposed technique and our experimental results shown that the approach is e cient and promising