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