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dc.contributor.authorCobb, Barry R.
dc.contributor.authorShenoy, Prakash P.
dc.date.accessioned2005-07-11T15:16:57Z
dc.date.available2005-07-11T15:16:57Z
dc.date.issued2005-07
dc.identifier.citationCobb, 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.
dc.identifier.isbn3-540-27326-3
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/1808/518
dc.description.abstractIn 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.
dc.format.extent174358 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringer-Verlag
dc.relation.ispartofseriesLecture Notes in Artificial Intelligence;3571
dc.subjectConditionally deterministic variables
dc.subjectMixtures of truncated exponentials
dc.subjectConditional linear gaussian distributions
dc.titleNonlinear Deterministic Relationships in Bayesian Networks
dc.typeBook chapter
kusw.oastatusna
dc.identifier.orcidhttps://orcid.org/0000-0002-8425-896X
kusw.oapolicyThis item does not meet KU Open Access policy criteria.
dc.rights.accessrightsopenAccess


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