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

Authors:
Changhe Yuan;Marek J. Druzdzel
Affiliations:
Mississippi State University, Mississippi State, MS 39762, USA;School of Information Sciences and Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15260, USA
Venue:
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Year:
2007

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Abstract

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 controltechnique 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 split-rejection control helps to achieve significantly better results.