On Disjunctive Representations of Distributions and Randomization
We study the usefulness of representing a given joint distribution as a positive linear combination of disjunctions of hypercubes, and generalize the associated results and techniques to Bayesian networks (BNs). The fundamental idea is to pre-compile a given distribution into this form, and employ a...
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Published in | Research and Development in Intelligent Systems XXI pp. 327 - 340 |
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Main Author | |
Format | Book Chapter |
Language | English |
Published |
London
Springer London
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Subjects | |
Online Access | Get full text |
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Summary: | We study the usefulness of representing a given joint distribution as a positive linear combination of disjunctions of hypercubes, and generalize the associated results and techniques to Bayesian networks (BNs). The fundamental idea is to pre-compile a given distribution into this form, and employ a host of randomization techniques at runtime to answer various kinds of queries efficiently. Generalizing to BNs, we show that these techniques can be effectively combined with the dynamic programming-based ideas of message-passing and clique-trees to exploit both the topology (conditional independence relationships between the variables) and the numerical structure (structure of the conditional probability tables) of a given BN in efficiently answering queries at runtime. |
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ISBN: | 1852339071 9781852339074 |
DOI: | 10.1007/1-84628-102-4_24 |