A Parallel Algorithm for Bayesian Network Parameter Learning Based on Factor Graph

Yue Zhao, Jungang Xu, Yunjun Gao

Abstract: Bayesian Network parameter learning is one of the core issues of Bayesian Network research. The parameter estimation of Bayesian Network from large incomplete dataset can be very compute-intensive. A factor graph based Bayesian Network parameter learning algorithm using MapReduce is presented in this paper, which decomposes one Bayesian Network into factors and gets the Bayesian Network parameter through computing the conditional probability tables of each factor independently using Expectation Maximization (EM) algorithm within MapReduce framework. Experimental results show that when the number of training samples is 107, the speed of this parallel algorithm can get 2~6 times the speed of Sequential Expectation Maximization. The algorithm can reduce the training time significantly with increasing the number of Hadoop nodes. Compared with the existing parallel EM method using MapReduce, this algorithm has also a higher speed and can avoid the problem of load imbalance at the same time.