Improving Importance Sampling by Adaptive Split-Rejection Control in Bayesian Networks

Importance sampling-based algorithms are a popular alternative when Bayesian network models are too large or too complex for exact algorithms. However, importance sampling is sensitive to the quality of the importance function. A bad importance function often leads to much oscillation in the sample weights, and, hence, poor estimation of the posterior probability distribution. To address this problem, we propose the adaptive split-rejection control technique to adjust the samples with extremely large or extremely small weights, which contribute most to the variance of an importance sampling estimator. Our results show that when we adopt this technique in the EPIS-BN algorithm [14], adaptive splitrejection control helps to achieve signi cantly better results.