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Proceedings Paper

Study of the most probable explanation in hybrid Bayesian networks
Author(s): Wei Sun; KC Chang
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Paper Abstract

In addition to computing the posterior distributions for hidden variables in Bayesian networks, one other important inference task is to find the most probable explanation (MPE). MPE provides the most likely configurations to explain away the evidence and helps to manage hypotheses for decision making. In recent years, researchers have proposed a few methods to find the MPE for discrete Bayesian networks. However, finding the MPE for hybrid networks remains challenging. In this paper, we first briefy review the current state-of-the-art in the literature regarding various explanation methods. We then present an algorithm by using a modified max-product clique tree to find the MPE for accommodating the needs in hybrid Bayesian networks. A detailed example is demonstrated to show the algorithm.

Paper Details

Date Published: 12 May 2011
PDF: 8 pages
Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 80500T (12 May 2011); doi: 10.1117/12.884039
Show Author Affiliations
Wei Sun, George Mason Univ. (United States)
KC Chang, George Mason Univ. (United States)

Published in SPIE Proceedings Vol. 8050:
Signal Processing, Sensor Fusion, and Target Recognition XX
Ivan Kadar, Editor(s)

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