基于因果语义定向的贝叶斯网络结构学习

Learning Bayesian networks structure based on causal semanitics orienting

基于变量之间基本依赖关系、基本结构、d-separation标准、依赖分析思想和混合定向策略,给出了一种有效实用的贝叶斯网络结构学习方法,不需要结点有序,并能避免打分-搜索方法存在的指数复杂性,以及现有依赖分析方法的大量高维条件概率计算等问题。

A new method of learning Bayesian network structure based on basic dependency relationship between variables,basic structure between nodes,d-separation criterion,the idea of dependency analysis and the strategy of mixture orienting is given.This method do not need sorting nodes.h can effectively avoid the exponential complexity of search ＆ scoring based methods and a large number of the calculate of high rank conditional probability in existing dependency analysis based methods.