Refractor Importance Sampling

In this paper we introduce Refractor Importance Sampling (RIS), an improvement to reduce error variance in Bayesian network importance sampling propagation under evidential reasoning. We prove the existence of a collection of importance functions that are close to the optimal importance function under evidential reasoning. Based on this theoretic result we derive the RIS algorithm. RIS approaches the optimal importance function by applying localized arc changes to minimize the divergence between the evidence-adjusted importance function and the optimal importance function. The validity and performance of RIS is empirically tested with a large set of synthetic Bayesian networks and two realworld networks.

• Type: Conference
• Year: 2008
• Where: UAI
• Authors: Haohai Yu, Robert van Engelen
• Keywords: Artificial Intelligence, Importance Function, Importance Sampling, Optimal Importance Function, UAI 2008