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Bucket Elimination: A Unifying Framework for Probabilistic Inference

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Learning in Graphical Models

Part of the book series: NATO ASI Series ((ASID,volume 89))

Abstract

Probabilistic inference algorithms for belief updating, finding the most probable explanation, the maximum a posteriori hypothesis, and the maximum expected utility are reformulated within the bucket elimination framework. This emphasizes the principles common to many of the algorithms appearing in the probabilistic inference literature and clarifies the relationship of such algorithms to nonserial dynamic programming algorithms. A general method for combining conditioning and bucket elimination is also presented. For all the algorithms, bounds on complexity are given as a function of the problem’s structure.

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Author information

Authors and Affiliations

  1. Department of Information and Computer Science, University of California, Irvine, USA

    R. Dechter

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  1. R. Dechter
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Editor information

Editors and Affiliations

  1. Massachusetts Institute of Technology, E25-229, Cambridge, MA, 02139, USA

    Michael I. Jordan

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© 1998 Springer Science+Business Media Dordrecht

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Dechter, R. (1998). Bucket Elimination: A Unifying Framework for Probabilistic Inference. In: Jordan, M.I. (eds) Learning in Graphical Models. NATO ASI Series, vol 89. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5014-9_4

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  • DOI: https://doi.org/10.1007/978-94-011-5014-9_4

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-6104-9

  • Online ISBN: 978-94-011-5014-9

  • eBook Packages: Springer Book Archive

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