Inference in Hybrid Bayesian Networks with Mixtures of Truncated Exponentials
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Issue Date
2005-06Author
Cobb, Barry R.
Shenoy, Prakash P.
Publisher
University of Kansas School of Business
Format
443794 bytes
Type
Working Paper
Is part of series
School of Business Working Paper;294
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Show full item recordAbstract
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.
Description
Has been accepted for publication in the International Journal of Approximate Reasoning, Elsevier Science Publishing Co., Inc.
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Citation
Cobb, B. R. and P. P. Shenoy (2005), "Inference in Hybrid Bayesian Networks with Mixtures of Truncated Exponentials," University of Kansas School of Business Working Paper No. 294, July 2003, Revised June 2005, Lawrence, KS.
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