KU ScholarWorks >
School of Business >
School of Business Articles >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1808/994
View usage statistics

Full metadata record

DC FieldValueLanguage
dc.contributor.authorCobb, Barry R.-
dc.contributor.authorShenoy, Prakash P.-
dc.contributor.authorRumi, Rafael-
dc.date.accessioned2006-07-11T02:33:50Z-
dc.date.available2006-07-11T02:33:50Z-
dc.date.issued2006-09-
dc.identifier.citationCobb, B. R., P. P. Shenoy, and R. Rumi "Approximating probability density functions in hybrid Bayesian networks with mixtures of truncated exponentials," Statistics and Computing, Vol. 16, No. 3, 2006, pp. 293--308.en
dc.identifier.issn0960-3174 (Paper)-
dc.identifier.issn1573-1375 (Online)-
dc.identifier.urihttp://hdl.handle.net/1808/994-
dc.description.abstractMixtures of truncated exponentials (MTE) potentials are an alternative to discretization and Monte Carlo methods for solving hybrid Bayesian networks. Any probability density function (PDF) can be approximated by 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 standard PDF’s and applications of these potentials for solving inference problems in hybrid Bayesian networks. These approximations will extend the types of inference problems that can be modeled with Bayesian networks, as demonstrated using three examples.en
dc.format.extent451910 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoenen
dc.publisherSpringer Netherlandsen
dc.subjectGraphs and networksen
dc.subjectprobabilistic computationen
dc.subjectmixtures of truncated exponentialsen
dc.subjecthybrid Bayesian networksen
dc.titleApproximating probability density functions in hybrid Bayesian networks with mixtures of truncated exponentialsen
dc.typeArticleen
Appears in Collections:School of Business Articles

Files in this Item:

File Description SizeFormat
SC06.pdf441.32 kBAdobe PDFView/Open