Bayesian Inference Based on Global Message Propagation
基于全局消息传播的贝叶斯推理

Uncertain probabilistie inference is often made in Bayesian network However,for a common complicated network,accurate inference algorithm is always deserted for its unpaid high cost of computing complexity.Aiming at this problem,this paper brings forward a nearly accurate inference algorithm PPJT.Newalgorithm applies the mecha- nism of passing message to update the potentials of Join tree's cliques by steps of message collection and message dis- tribution and eventually generates a consistent join tree.Compared with another nearly accurate inference algorithm, namely likelihood weighting algorithm,the time-using performance experimentation shows that PPJT decreases the time complexity efficiently.At the same time,PPJT improves the uncertain inference accuracy.The experimentation for computing accuracy comparison shows that,under relative small samples input,PPJT can ensure much higher accu- racy for inference.PPJT provides a new theoretic tool for implementation of probabilistic inference in the common com- plicated network.