Particle filter with iterative importance sampling for Bayesian networks inference

Bayesian network has been applied widely in many areas such as multi-sensor fusion, situation assessment, and decision making under uncertainty. It is well known that, in general when dealing with large complex networks, the exact probabilistic inference methods are computationally difficult or impossible. To deal with the difficulty, the "anytime" stochastic simulation methods such as likelihood weighting and importance sampling have become popular. In this paper, we introduce a very efficient iterative importance sampling algorithm for Bayesian network inference. Much like the recently popular sequential simulation method, particle filter, this algorithm identifies importance function and conducts sampling iteratively. However, particle filter methods often run into the so called "degeneration" or "impoverishment" problems due to low likely evidence or high dimensional sampling space. To overcome that, this Bayesian network particle filter (BNPF) algorithm decomposes the global state space into local ones based on the network structure and learns the importance function accordingly in an iterative manner. We used large real world Bayesian network models available in academic community to test the inference method. The preliminary simulation results show that the algorithm is very promising.