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Inference in Hybrid Bayesian Networks with Deterministic Variables

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Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5590))

Abstract

The main goal of this paper is to describe an architecture for solving large general hybrid Bayesian networks (BNs) with deterministic variables. In the presence of deterministic variables, we have to deal with non-existence of joint densities. We represent deterministic conditional distributions using Dirac delta functions. Using the properties of Dirac delta functions, we can deal with a large class of deterministic functions. The architecture we develop is an extension of the Shenoy-Shafer architecture for discrete BNs. We illustrate the architecture with some small illustrative examples.

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Authors and Affiliations

  1. University of Kansas School of Business, 1300 Sunnyside Ave., Summerfield Hall, Lawrence, KS 66045-7585, USA

    Prakash P. Shenoy & James C. West

Authors
  1. Prakash P. Shenoy
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  2. James C. West
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Editor information

Editors and Affiliations

  1. ISIB-CNR, Corso Stati Uniti 4, 35127, Padova, Italy

    Claudio Sossai

  2. ISIB-CNR, Corso Stati Uniti, 4, 35127, Padova, Italy

    Gaetano Chemello

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Shenoy, P.P., West, J.C. (2009). Inference in Hybrid Bayesian Networks with Deterministic Variables. In: Sossai, C., Chemello, G. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2009. Lecture Notes in Computer Science(), vol 5590. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02906-6_6

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  • DOI: https://doi.org/10.1007/978-3-642-02906-6_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02905-9

  • Online ISBN: 978-3-642-02906-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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