
Learning Bayesian Networks Using a Parallel EM Approach
YU Kui, WANG Hao, WU Xin-Dong, YAO Hong-Liang
Learning Bayesian Networks Using a Parallel EM Approach
Computing the expected statistics is the main bottleneck in learning Bayesian networks. Firstly, a parallel expectation-maximization (PL-EM) algorithm for leaning Bayesian network parameters is presented. The PL-EM algorithm parallelizes the E-step and M-step and the greatly reduces the time complexity of the parameter learning. Then PL-EM algorithm is applied to learning Bayesian networks structure, and a parallel learning algorithm is proposed for learning Bayesian networks based on an existing structural EM algorithm (SEM), called PL-SEM. PL-SEM exploits PL-EM algorithm to compute the expected statistics at the structural E_Step. Thus, it can implement the parallel computation of the expected statistics and greatly reduce the time complexity of learning Bayesian networks.
Bayesian Networks / Parameter Learning / Structural Learning / Expectation-Maximization (EM) Algorithm / Message Passing Interface (MPI) Library {{custom_keyword}} /
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