Causality enabled compositional modelling of Bayesian networks

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dc.contributor.author Shen, Qiang
dc.contributor.author Keppens, Jeroen
dc.date.accessioned 2008-01-15T20:48:09Z
dc.date.available 2008-01-15T20:48:09Z
dc.date.issued 2004
dc.identifier.citation Shen , Q & Keppens , J 2004 , ' Causality enabled compositional modelling of Bayesian networks ' pp. 33-40 . en
dc.identifier.other PURE: 74789
dc.identifier.other dspace: 2160/428
dc.identifier.uri http://hdl.handle.net/2160/428
dc.description J. Keppens and Q. Shen. Causality enabled compositional modelling of Bayesian networks. Proceedings of the 18th International Workshop on Qualitative Reasoning, pages 33-40, 2004. en
dc.description.abstract Probabilistic abduction extends conventional symbolic abductive reasoning with Bayesian inference methods. This allows for the uncertainty underlying implications to be expressed with probabilities as well as assumptions, thus complementing the symbolic approach in situations where the use of a complete list of assumptions underlying inferences is not practical. However, probabilistic abduction has been of little use in first principle-based applications, such as abductive diagnosis, largely because no methods are available to automate the construction of probabilistic models, such as Bayesian networks (BNs). This paper addresses this issue by proposing a compositional modelling method for BNs. en
dc.format.extent 8 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Causality enabled compositional modelling of Bayesian networks en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Non peer reviewed en


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