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Please use this identifier to cite or link to this item: http://hdl.handle.net/1808/902
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Title: Inference in hybrid Bayesian networks wih mixtures of truncated exponenials
Authors: Cobb, Barry R.
Shenoy, Prakash P.
Keywords: Hybrid Bayesian networks
mixtures of truncated exponentials
Shenoy-Shafer architecture
conditional linear Gaussian models
Issue Date: Apr-2006
Publisher: Elsevier
Extent: 570071 bytes
Type: Article
Citation: Cobb, B. R., and P. P. Shenoy, "Inference in hybrid Bayesian networks wih mixtures of truncated exponenials," International Journal of Approximate Reasoning, Vol. 41, No. 3, April 2006, pp. 257--286.
Abstract: Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for solving hybrid Bayesian networks. Any probability density function (PDF) can be approximated with an MTE potential, which can always be marginalized in closed form. This allows propagation to be done exactly using the Shenoy-Shafer architecture for computing marginals, with no restrictions on the construction of a join tree. This paper presents MTE potentials that approximate an arbitrary normal PDF with any mean and a positive variance. The properties of these MTE potentials are presented, along with examples that demonstrate their use in solving hybrid Bayesian networks. Assuming that the joint density exists, MTE potentials can be used for inference in hybrid Bayesian networks that do not fit the restrictive assumptions of the conditional linear Gaussian (CLG) model, such as networks containing discrete nodes with continuous parents.
URI: http://hdl.handle.net/1808/902
ISSN: 0888-613X
Appears in Collections:School of Business Articles

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