A Sampling Approximate Inference Algorithm Based on Decomposition of Markov Blanket
Hao Wang · 2013
Current inference algorithms of Bayesian networks are weak on inference precision and inference time to a certain degree.Therefore,in this paper a practical and reliable inference method,sampling approximate inference algorithm based on Markov blanket(LSIA-MB),is presented.Firstly,HITON_MB algorithm is utilized to obtain the Markov blanket of the query node and then the dynamic programming algorithm is used to learn the posterior probability of edges to get a Markov local network model of the query node.Finally,Gibbs sampling inference algorithm is executed on the Markov local model.The sampling on the local model significantly reduces the calculation dimensions.The inference precision is retained because the Markov local model contains the complete information associated with the query node.Algorithm analysis and experimental results on standard Alarm network show LSIA-MB algorithm significantly reduces the inference time and improves the inference precision.The inference results of LSIA-MB algorithm on the Shanghai stock exchange network show the algorithm has strong practicability.