Learning Bayesian network structure with missing data

At present,the algorithm of learning Bayesian structure with missing data is mainly based on the search and scoring method combined with EM algorithm.The algorithm had low efficiency.A new algorithm of learning Bayesian network structure with missing data is presented.KL divergence is used to express the similarity between the cases.Then the value of the missing data is draw according to the Gibbs sampling.Finally,heuristic search is used to complete the learning of Bayesian network structure.This method can avoid the exponential complexity of standard Gibbs sampling and the main problems in the existing algorithm.