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Conference Paper: Fuzzy Bayesian inference

TitleFuzzy Bayesian inference
Authors
KeywordsComputers
Cybernetics
Issue Date1997
PublisherIEEE.
Citation
Computational Cybernetics and Simulation, IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings, Orlando, Florida, USA, 12-15 October 1997, v. 3, p. 2707-2712 How to Cite?
AbstractBayesian methods provide formalism for reasoning about partial beliefs under conditions of uncertainty. Given a set of exhaustive and mutually exclusive hypotheses, one can compute the probability of a hypothesis for a given evidence using the Bayesian inversion formula. In Bayesian's inference, the evidence could be a single atomic proposition or multi-valued one. For the multi-valued evidence, these values could be discrete, continuous, or fuzzy. For the continuous-valued evidence, the density functions used in the Bayesian inference are difficult to be determined in many practical situations. Complicated laboratory testing and advance statistical techniques are required to estimate the parameters of the assumed type of distribution. Using the proposed fuzzy Bayesian approach, a formulation is derived to estimate the density function from the conditional probabilities of the fuzzy-supported values. It avoids the complicated testing and analysis, and it does not require the assumption of a particular type of distribution. The estimated density function in our approach is proved to conform to two axioms in the theorem of probability. Example is provided in the paper.
Persistent Identifierhttp://hdl.handle.net/10722/45592
ISSN

 

DC FieldValueLanguage
dc.contributor.authorYang, CCCen_HK
dc.date.accessioned2007-10-30T06:29:52Z-
dc.date.available2007-10-30T06:29:52Z-
dc.date.issued1997en_HK
dc.identifier.citationComputational Cybernetics and Simulation, IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings, Orlando, Florida, USA, 12-15 October 1997, v. 3, p. 2707-2712en_HK
dc.identifier.issn1062-922Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/45592-
dc.description.abstractBayesian methods provide formalism for reasoning about partial beliefs under conditions of uncertainty. Given a set of exhaustive and mutually exclusive hypotheses, one can compute the probability of a hypothesis for a given evidence using the Bayesian inversion formula. In Bayesian's inference, the evidence could be a single atomic proposition or multi-valued one. For the multi-valued evidence, these values could be discrete, continuous, or fuzzy. For the continuous-valued evidence, the density functions used in the Bayesian inference are difficult to be determined in many practical situations. Complicated laboratory testing and advance statistical techniques are required to estimate the parameters of the assumed type of distribution. Using the proposed fuzzy Bayesian approach, a formulation is derived to estimate the density function from the conditional probabilities of the fuzzy-supported values. It avoids the complicated testing and analysis, and it does not require the assumption of a particular type of distribution. The estimated density function in our approach is proved to conform to two axioms in the theorem of probability. Example is provided in the paper.en_HK
dc.format.extent355348 bytes-
dc.format.extent3082 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©1997 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subjectComputersen_HK
dc.subjectCyberneticsen_HK
dc.titleFuzzy Bayesian inferenceen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1062-922X&volume=3&spage=2707&epage=2712&date=1997&atitle=Fuzzy+Bayesian+inferenceen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICSMC.1997.635347en_HK
dc.identifier.hkuros31122-

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