Generalized Evidence Pre-propagated Importance Sampling for Hybrid Bayesian Networks

Changhe Yuan, Marek J. Druzdzel

In this paper, we first provide a new theoretical understanding of the Evidence Pre-propagated Importance Sampling algorithm(EPIS-BN) and show that its importance function minimizes the KL-divergence between the function itself and the exact posterior probability distribution in Polytrees. We then generalize the method to deal with inference in general hybrid Bayesian networks consisting of deterministic equations and arbitrary probability distributions. Using a novel technique called soft arc reversal, the new algorithm can also handle evidential reasoning with observed deterministic variables.

Subjects: 3.4 Probabilistic Reasoning; 15.8 Simulation

Submitted: Apr 24, 2007

This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.