Abstract In this paper,a local Gibbs sampling inference algorithm of Bayesian networks (S-LGSI) is proposed. Firstly,the S-LGSI algorithm precisely decomposes Bayesian networks based on the analytic idea of junction tree algorithm. Secondly,the suitable local model is chosen by the query node and the evidence node. Then,Gibbs sampling inference algorithm for local network model is utilized. Compared with other current approximate sampling algorithms,the S-LGSI algorithm significantly reduces the calculation dimension. The sampling inference in the local model avoids the statistics of joint sample series and greatly reduces the calculation dimension. The proposed algorithm guarantees the inference precision,as the local model contains important information about the query node. Algorithm analysis and experimental results on Alarm network show S-LGSI significantly reduces the complexity and improves the inference precision. The proposed algorithm has strong practicability,because the inference results of S-LGSI algorithm are basically consistent with the real situation on the Shanghai Stock Exchange network.
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