Learning Bayesian network equivalence classes based on mutual information
基于互信息学习贝叶斯网络等价类*

Constructing Bayesian network structures from data is NP-hard.According to the mutual information and conditional independence test,this paper presented a new algorithm for the construction of the optimal Bayesian network structure.Numerical experiments show that the new algorithm can determined much faster the structure with highest degree of data matching,thus the study of Bayesian network structures become more efficient.