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Representation and Reasoning for Recursive Probability Models

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dc.contributor.authorHoward, C.M.
dc.contributor.authorStumptner, M.
dc.contributor.conference19th Australian Joint Conference on Artificial Intelligence (4 Dec 2006 - 8 Dec 2006 : Hobart, Australia)
dc.contributor.editorSattar, A.
dc.contributor.editorKang, B.H.
dc.date.issued2006
dc.description.abstractThis paper applies the Object Oriented Probabilistic Relational Modelling Language to recursive probability models. We present two novel anytime inference algorithms for recursive probability models expressed using this language. We discuss the strengths and limitations of these algorithms and compare their performance against the Iterative Structured Variable Elimination algorithm proposed for Probabilistic Relational Modelling Language using three different non-linear genetic recursive probability models. © Springer-Verlag Berlin Heidelberg 2006.
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006 / Sattar, A., Kang, B.H. (ed./s), vol.4304 LNAI, pp.120-130
dc.identifier.doi10.1007/11941439_16
dc.identifier.isbn9783540497875
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttps://hdl.handle.net/1959.8/42408
dc.language.isoen
dc.publisherSpringer-Verlag
dc.publisher.placeUnited States
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.rightsCopyright status unknown
dc.source.urihttps://doi.org/10.1007/11941439_16
dc.titleRepresentation and Reasoning for Recursive Probability Models
dc.typeConference paper
pubs.publication-statusPublished
ror.mmsid9915911792701831

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