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dc.contributor.authorCobb, Barry R.
dc.contributor.authorShenoy, Prakash P.
dc.date.accessioned2004-12-20T14:52:52Z
dc.date.available2004-12-20T14:52:52Z
dc.date.issued2004-10
dc.identifier.citationCobb, B. R. and P. P. Shenoy, "Inference in Hybrid Bayesian Networks with Deterministic Variables," in P. Lucas (ed.), Proceedings of the Second European Workshop on Probabilistic Graphical Models (PGM-04), 2004, pp. 57--64, Lorentz Center, Leiden, Netherlands.
dc.identifier.urihttp://hdl.handle.net/1808/176
dc.description.abstractAn important class of hybrid Bayesian networks are those that have conditionally deterministic variables (a variable that is a deterministic function of its parents). In this case, if some of the parents are continuous, then the joint density function does not exist. Conditional linear Gaussian (CLG) distributions can handle such cases when the deterministic function is linear and continuous variables are normally distributed. In this paper, we develop operations required for performing inference with conditionally deterministic variables using relationships derived from joint cumulative distribution functions (CDF’s). These methods allow inference in networks with deterministic variables where continuous variables are non-Gaussian.
dc.format.extent208244 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectHybrid Bayesian networks
dc.subjectConditionally deterministic variables
dc.subjectMixtures of truncated exponentials
dc.subjectConditional linear gaussian distributions
dc.titleInference in Hybrid Bayesian Networks with Deterministic Variables
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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