Estimating bayesian networks parameters using EM and Gibbs sampling

A method based on Expectation Maximization (EM) algorithm and Gibbs sampling is proposed to estimate Bayesian networks (BNs) parameters. We employ the Gibbs sampling to approximate the E-step of EM algorithm. According to transition probability, Gibbs sampling is utilized in data completion of E-step, which can reduce the computational complexity of EM algorithm. The experiments for comparison between the proposed method and EM algorithm are made. For the proposed method, the consumed time and the number of iterations are all less than those of EM algorithm. However, the KL divergence is higher than that of EM algorithm, which is a limitation for the proposed method.