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    Axioms for Probability and Belief-Function Propagation

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    UAI90.pdf (233.7Kb)
    Issue Date
    1990
    Author
    Shenoy, Prakash P.
    Shafer, Glenn R.
    Publisher
    Elsevier Science Publishers B. V.
    Format
    239375 bytes
    Type
    Book chapter
    Is part of series
    Machine Intelligence and Pattern Recognition;Volume 9
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    Abstract
    In this paper, we describe an abstract framework and axioms under which exact local computation of marginals is possible. The primitive objects of the framework are variables and valuations. The primitive operators of the framework are combination and marginalization. These operate on valuations. We state three axioms for these operators and we derive the possibility of local computation from the axioms. Next, we describe a propagation scheme for computing marginals of a valuation when we have a factorization of the valuation on a hypertree. Finally we show how the problem of computing marginals of joint probability distributions and joint belief functions fits the general framework
    Description
    This article was reprinted in G. Shafer and J. Pearl (eds.), Readings in Uncertain Reasoning, 1990, pp. 575-610, Morgan Kaufmann, San Mateo, CA. Also, a condensed 8-pp version of this paper appeared in the Proceedings of the Fourth Workshop on Uncertainty in Artificial Intelligence in 1988.
    URI
    http://hdl.handle.net/1808/144
    ISBN
    0 444 88650 8
    Collections
    • Distinguished Professors Scholarly Works [918]
    • School of Business Scholarly Works [212]
    Citation
    In R. D. Shachter, T. S. Levitt, L. N. Kanal and J. F. Lemmer (eds.), Uncertainty in Artificial Intelligence 4, 1990, 169--198, North-Holland, Amsterdam.

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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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