Data Missing Bayesian Network Parameters Learning Optimization Algorithm Based on EM

Abstract

The paper proposes an optimization algorithm for the parameter learning of missing data Bayesian networks. Expectation Maximization (EM) algorithm is the common parameter learning algorithms. The maximum likelihood estimation (MLE) and maximum a posterity estimation (MAP) of EM are local estimate rather than global estimate and are not easy to achieve the global optimal. So this paper puts forward a point estimate relative error minimum optimization algorithm which based on EM algorithm (EM-MLE-MAP). Applying the algorithm to the fault diagnosis of the rotor vibration Bayesian network, simulations and experiments show that the improved algorithm has good precision in diagnosing vibration fault when the loss ratio less than 3 percent.