Axioms for Probability and Belief-Function Propagation

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Issue Date
1990Author
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
Metadata
Show full item recordAbstract
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.
ISBN
0 444 88650 8Collections
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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