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Conference Paper: Chain event graph map model selection

TitleChain event graph map model selection
Authors
KeywordsBayesian network
Chain event graph
Conjugate learning
Maximum a posteriori model
Issue Date2009
PublisherInstitute for Systems and Technologies of Information, Control and Communication.
Citation
The 1st International Conference on Knowledge Engineering and Ontology Development, Madeira, Portugal, 6-8 October 2009. In Proceedings of the 1st International Conference on Knowledge Engineering and Ontology Development, 2009, p. 392-395 How to Cite?
AbstractWhen looking for general structure from a finite discrete data set one can search over the class of Bayesian Networks (BNs). The class of Chain Event Graph (CEG) models is however much more expressive and is particularly suited to depicting hypotheses about how situations might unfold. Like the BN, the CEG admits conjugate learning on its conditional probability parameters using product Dirichlet priors. The Bayes Factors associated with different CEG models can therefore be calculated in an explicit closed form, which means that search for the maximum a posteriori (MAP) model in this class can be enacted by evaluating the score function of successive models and optimizing. Local search algorithms can be devised for the class of candidate models, but in this paper we concentrate on the process of scoring the members of this class.
Persistent Identifierhttp://hdl.handle.net/10722/176512
ISBN

 

DC FieldValueLanguage
dc.contributor.authorThwaites, PA-
dc.contributor.authorFreeman, G-
dc.contributor.authorSmith, JQ-
dc.date.accessioned2012-11-30T06:50:59Z-
dc.date.available2012-11-30T06:50:59Z-
dc.date.issued2009-
dc.identifier.citationThe 1st International Conference on Knowledge Engineering and Ontology Development, Madeira, Portugal, 6-8 October 2009. In Proceedings of the 1st International Conference on Knowledge Engineering and Ontology Development, 2009, p. 392-395-
dc.identifier.isbn978-989674012-2-
dc.identifier.urihttp://hdl.handle.net/10722/176512-
dc.description.abstractWhen looking for general structure from a finite discrete data set one can search over the class of Bayesian Networks (BNs). The class of Chain Event Graph (CEG) models is however much more expressive and is particularly suited to depicting hypotheses about how situations might unfold. Like the BN, the CEG admits conjugate learning on its conditional probability parameters using product Dirichlet priors. The Bayes Factors associated with different CEG models can therefore be calculated in an explicit closed form, which means that search for the maximum a posteriori (MAP) model in this class can be enacted by evaluating the score function of successive models and optimizing. Local search algorithms can be devised for the class of candidate models, but in this paper we concentrate on the process of scoring the members of this class.-
dc.languageeng-
dc.publisherInstitute for Systems and Technologies of Information, Control and Communication.-
dc.relation.ispartofProceedings of the 1st International Conference on Knowledge Engineering and Ontology Development-
dc.subjectBayesian network-
dc.subjectChain event graph-
dc.subjectConjugate learning-
dc.subjectMaximum a posteriori model-
dc.titleChain event graph map model selectionen_US
dc.typeConference_Paperen_US
dc.identifier.emailFreeman, G: gfreeman@hku.hk-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-77955409471-
dc.identifier.spage392-
dc.identifier.epage395-
dc.publisher.placeMadeira, Portugal-
dc.description.otherThe 1st International Conference on Knowledge Engineering and Ontology Development, Madeira, Portugal, 6-8 October 2009. In Proceedings of the 1st International Conference on Knowledge Engineering and Ontology Development, 2009, p. 392-395-

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