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Efficient Representation and Fusion of Hybrid Joint Densities for Clusters in Nonlinear Hybrid Bayesian Networks

Schrempf, Oliver C.; Hanselmann, Anne; Hanebeck, Uwe D.

Undirected cycles in Bayesian networks are often treated by using clustering methods. This results in networks with nodes characterized by joint probability densities instead of marginal densities. An efficient representation of these hybrid joint densities is essential especially in nonlinear hybrid net works containing continuous as well as discrete variables. In this article we present a unified representation of continuous, discrete, and hybrid joint densities. This representation is based on Gaussian and Dirac mixtures and allows for analytic evaluation of arbitrary hybrid networks without loosing structural in formation, even for networks containing clusters. Furthermore we derive update formulae for marginal and joint densities from a system theoretic point of view by treating a Bayesian network as a system of cascaded subsystems. Together with the presented mixture representation of densities this yields an exact analytic updating scheme.

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Volltext §
DOI: 10.5445/IR/1000013903
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2006
Sprache Englisch
Identifikator ISBN: 1-424-40953-5
KITopen-ID: 1000013903
Erschienen in Proceedings / 9th International Conference on Information Fusion, July 2006, Florence, Italy
Verlag IEEE Service Center, Piscataway (NJ)
Seiten 1 - 8
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