Abstract
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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© 2005 Springer-Verlag London Limited
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Satish Kumar, T.K. (2005). On Disjunctive Representations of Distributions and Randomization. In: Bramer, M., Coenen, F., Allen, T. (eds) Research and Development in Intelligent Systems XXI. SGAI 2004. Springer, London. https://doi.org/10.1007/1-84628-102-4_24
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DOI: https://doi.org/10.1007/1-84628-102-4_24
Publisher Name: Springer, London
Print ISBN: 978-1-85233-907-4
Online ISBN: 978-1-84628-102-0
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