Learning Bayesian Networks Structure with Hidden Variables
WANG ShuangCheng1,2, LIU XiHua3, TANG HaiYan2
1.Department of Information Science, Shanghai Lixin University of Commerce, Shanghai 201620 2.Risk Management Research Institute, Shanghai Lixin University of Commerce, Shanghai 201620 3.Economic Institute, Qingdao University, Qingdao 266071
Abstract At present the method of learning Bayesian network structure with hidden variables is mainly based on the scoringsearch method combined with EM algorithm. But it is inefficient and unreliable. A new method of learning Bayesian network structure with hidden variables is presented. In this method, the Bayesian network structure without hidden variables is set up based on basic dependency relationship between variables and basic structures between nodes and dependency analysis idea. Hidden variables are found in terms of the dimension of cliques in the moral graph of Bayesian network. The value, the dimension and the local structure of hidden variables are made based on dependency strcture between variables, Gibbs sampling and MDL criterion. The method can avoide the exponential complexity of standard Gibbs sampling and the main problems of the existing algorithm of learning Bayesian network structure with hidden variables. Experimental results show that this algorithm can effectively learn Bayesian network strcture with hidden variables.
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2006, Vol. 19 
