Study of the most probable explanation in hybrid Bayesian networks

Wei Sun; KC Chang

doi:10.1117/12.884039

12 May 2011 Study of the most probable explanation in hybrid Bayesian networks

Wei Sun, KC Chang

Proceedings Volume 8050, Signal Processing, Sensor Fusion, and Target Recognition XX; 80500T (2011) https://doi.org/10.1117/12.884039
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

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.

Citation Download Citation

Wei Sun and KC Chang "Study of the most probable explanation in hybrid Bayesian networks", Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 80500T (12 May 2011); https://doi.org/10.1117/12.884039

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