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Please use this identifier to cite or link to this item: http://hdl.handle.net/1808/518
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Title: Nonlinear Deterministic Relationships in Bayesian Networks
Authors: Cobb, Barry R.
Shenoy, Prakash P.
Keywords: Conditionally deterministic variables
mixtures of truncated exponentials
conditional linear Gaussian distributions
Issue Date: Jul-2005
Publisher: Springer-Verlag
Extent: 174358 bytes
Type: Book chapter
Citation: Cobb, B. R. and P. P. Shenoy (2005), "Nonlinear Deterministic Relationships in Bayesian Networks," in L. Godo (ed.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Lecture Notes in Artificial Intelligence 3571, 27--38, Springer-Verlag, Berlin.
Series/Report no.: Lecture Notes in Artificial Intelligence;3571
Abstract: In a Bayesian network with continuous variables containing a variable(s) that is a conditionally deterministic function of its continuous parents, the joint density function does not exist. Conditional linear Gaussian distributions can handle such cases when the deterministic function is linear and the continuous variables have a multi-variate normal distribution. In this paper, operations required for performing inference with nonlinear conditionally deterministic variables are developed. We perform inference in networks with nonlinear deterministic variables and non-Gaussian continuous variables by using piecewise linear approximations to nonlinear functions and modeling probability distributions with mixtures of truncated exponentials (MTE) potentials.
URI: http://hdl.handle.net/1808/518
ISBN: 3-540-27326-3
ISSN: 0302-9743
Appears in Collections:School of Business Articles

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