基于Markov毯分解的抽样近似推理算法

现有的贝叶斯推理算法不同程度地存在推理精度低或推理时间长的问题。文中提出一种基于Markov毯分解的抽样近似推理算法(LSIA-MB)。LSIA-MB算法利用HITON_MB算法寻找查询结点的Markov毯, 进而利用动态规划方法学习边的后验概率, 确定变量之间的因果关系, 获得一个关于查询结点的Markov局部网络模型。最后, 在Markov局部模型上执行Gibbs Sampling。通过对Markov局部模型的抽样, 极大降低推理的计算维数。同时, 由于Markov局部网络模型包含与目标结点相关的完整信息, 从而保证局部抽样推理的精度。算法分析和在标准Alarm网的实验结果均表明, LSIA-MB算法降低推理时间, 且提高推理精度。LSIA-MB算法在上海股票交易网络上的推理预测结果显示出较强的实用性。

A Sampling Approximate Inference Algorithm Based on Decomposition of Markov Blanket

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, samplingapproximate 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.