基于马尔科夫毯约束的最优贝叶斯网络结构学习算法

本文针对最优贝叶斯网络的结构学习问题，在动态规划算法（Dynamic Programming，DP）的基础上，使用IAMB算法（Incremental Association Markov Blanket，IAMB）计算得到的马尔科夫毯对评分计算过程进行约束，减少了评分的计算次数，提出了基于马尔科夫毯约束的动态规划算法（Dynamic Programming Constrained with Markov Blanket，DPCMB），研究了IAMB算法中重要性阈值对DPCMB算法的各项性能指标的影响，给出了调整阈值的合理建议.实验结果表明，DPCMB算法可以通过调整重要性阈值，使该算法的精度与DP算法相当，极大地减少了算法的运行时间、评分计算次数和所需存储空间.

Learning Optimal Bayesian Network Structure Constrained with Markov Blanket

To solve the problem about structure learning of optimal Bayesian network,this paper proposes dynamic programming constrained with Markov blanket (DPCMB),which uses Markov blanket calculated by incremental association Markov blanket (IAMB) to reduce the number of scoring calculations in dynamic programming.We research on the effect of the significance value in IAMB on the performance indicators of DPCMB algorithm,and give reasonable suggestions for adjusting the significance value.Experimental results show that the DPCMB algorithm can adjust the significance value so that the accuracy of the algorithm is comparable to that of the DP algorithm,and running time,score calculation times,and memory requirements of the algorithm are greatly reduced.