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    Practical Aspects of Solving Hybrid Bayesian Networks Containing Deterministic Conditionals

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    Issue Date
    2015-03
    Author
    Shenoy, Prakash P.
    Rumi, Rafael
    Salmeron, Antonio
    Publisher
    Wiley
    Type
    Article
    Article Version
    Scholarly/refereed, author accepted manuscript
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    Abstract
    In this paper we discuss some practical issues that arise in solv- ing hybrid Bayesian networks that include deterministic conditionals for continuous variables. We show how exact inference can become intractable even for small networks, due to the di culty in handling deterministic conditionals (for continuous variables). We propose some strategies for carrying out the inference task using mixtures of polyno- mials and mixtures of truncated exponentials. Mixtures of polynomials can be de ned on hypercubes or hyper-rhombuses. We compare these two methods. A key strategy is to re-approximate large potentials with potentials consisting of fewer pieces and lower degrees/number of terms. We discuss several methods for re-approximating potentials. We illustrate our methods in a practical application consisting of solv- ing a stochastic PERT network.
    Description
    This is the author's final draft. Copyright 2015 Wiley
    URI
    http://hdl.handle.net/1808/18515
    DOI
    https://doi.org/10.1002/int.21700
    Collections
    • School of Business Scholarly Works [212]
    Citation
    Shenoy, Prakash P., Rafael Rumí, and Antonio Salmerón. "Practical Aspects of Solving Hybrid Bayesian Networks Containing Deterministic Conditionals." International Journal of Intelligent Systems Int. J. Intell. Syst. 30.3 (2014): 265-91. doi: http://dx.doi.org/10.1002/int.21700.

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    785-864-8983

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    Lawrence, KS 66045
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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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