Learning Decomposable Markov Network Structure with Missing Data

It is an important and difficult research project to learn decomposable Markov network structure with missing data. Missing data makes the dependency relationship between variables more disordered and it impossible to learn decomposable Markov network structure directly. In this paper, Gibbs sampling is combined with maximum likelihood tree to modify the missing data randomly initialized and regulate maximum likelihood tree iteratively so as to get complete data set. Using complete data set, the decomposable Markov network structure can be learned based on basic dependency relationship between variables and the idea of dependency analysis. The problems of low efficiency and reliability in existing methods of dealing with missing data and learning decomposable Markov network structure can be avoided. Experimental results show that this method can effectively learn decomposable Markov network structure with missing data.