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Conference Paper: Chain event graph map model selection
Title | Chain event graph map model selection |
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Authors | |
Keywords | Bayesian network Chain event graph Conjugate learning Maximum a posteriori model |
Issue Date | 2009 |
Publisher | Institute 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? |
Abstract | When 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 Identifier | http://hdl.handle.net/10722/176512 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Thwaites, PA | - |
dc.contributor.author | Freeman, G | - |
dc.contributor.author | Smith, JQ | - |
dc.date.accessioned | 2012-11-30T06:50:59Z | - |
dc.date.available | 2012-11-30T06:50:59Z | - |
dc.date.issued | 2009 | - |
dc.identifier.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 | - |
dc.identifier.isbn | 978-989674012-2 | - |
dc.identifier.uri | http://hdl.handle.net/10722/176512 | - |
dc.description.abstract | When 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.language | eng | - |
dc.publisher | Institute for Systems and Technologies of Information, Control and Communication. | - |
dc.relation.ispartof | Proceedings of the 1st International Conference on Knowledge Engineering and Ontology Development | - |
dc.subject | Bayesian network | - |
dc.subject | Chain event graph | - |
dc.subject | Conjugate learning | - |
dc.subject | Maximum a posteriori model | - |
dc.title | Chain event graph map model selection | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Freeman, G: gfreeman@hku.hk | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.scopus | eid_2-s2.0-77955409471 | - |
dc.identifier.spage | 392 | - |
dc.identifier.epage | 395 | - |
dc.publisher.place | Madeira, Portugal | - |
dc.description.other | 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 | - |