An importance sampling algorithm based on evidence pre-propagation

Authors:
Changhe Yuan;Marek J. Druzdzel
Affiliations:
Decision Systems Laboratory, School of Information Sciences and Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA;Decision Systems Laboratory, School of Information Sciences and Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA
Venue:
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Year:
2002

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Abstract

Precision achieved by stochastic sampling algorithms for Bayesian networks typically deteriorates in face of extremely unlikely evidence. To address this problem, we propose the Evidence Pre-propagation Importance Sampling algorithm (EPIS-BN), an importance sampling algorithm that computes an approximate importance function using two techniques: loopy belief propagation [19, 25] and ε-cutoff heuristic [2]. We tested the performance of EPIS-BN on three large real Bayesian networks: ANDES [3], CPCS [21], and PATHFINDER[11]. We observed that on each of these networks the EPIS-BN algorithm outperforms AISBN [2], the current state of the art algorithm, while avoiding its costly learning stage.