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Optimizing Inference in Bayesian Networks and Semiring Valuation Algebras

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MICAI 2007: Advances in Artificial Intelligence (MICAI 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4827))

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Abstract

Previous work on context-specific independence in Bayesian networks is driven by a common goal, namely to represent the conditional probability tables in a most compact way. In this paper, we argue from the view point of the knowledge compilation map and conclude that the language of Ordered Binary Decision Diagrams (OBDD) is the most suitable one for representing probability tables, in addition to the language of Algebraic Decision Diagrams (ADD). We thus suggest the replacement of the current practice of using tree-based or rule-based representations. This holds not only for inference in Bayesian networks, but is more generally applicable in the generic framework of semiring valuation algebras, which can be applied to solve a variety of inference and optimization problems in different domains.

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Author information

Authors and Affiliations

  1. University of Bern, Switzerland

    Michael Wachter

  2. Bern University of Applied Sciences, Switzerland

    Rolf Haenni

  3. University of Fribourg, Switzerland

    Marc Pouly

Authors
  1. Michael Wachter
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  2. Rolf Haenni
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  3. Marc Pouly
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Editor information

Alexander Gelbukh Ángel Fernando Kuri Morales

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Wachter, M., Haenni, R., Pouly, M. (2007). Optimizing Inference in Bayesian Networks and Semiring Valuation Algebras. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_23

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  • DOI: https://doi.org/10.1007/978-3-540-76631-5_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76630-8

  • Online ISBN: 978-3-540-76631-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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