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Proceedings Paper

Particle filter with iterative importance sampling for Bayesian networks inference
Author(s): K. C. Chang; Donghai He
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Paper Abstract

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.

Paper Details

Date Published: 25 May 2005
PDF: 9 pages
Proc. SPIE 5809, Signal Processing, Sensor Fusion, and Target Recognition XIV, (25 May 2005); doi: 10.1117/12.606063
Show Author Affiliations
K. C. Chang, George Mason Univ. (United States)
Donghai He, George Mason Univ. (United States)


Published in SPIE Proceedings Vol. 5809:
Signal Processing, Sensor Fusion, and Target Recognition XIV
Ivan Kadar, Editor(s)

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