Show simple item record

dc.contributorThe Pennsylvania State University CiteSeerX Archives
dc.contributor.authorK. S. Ng
dc.contributor.authorJ. W. Lloyd
dc.contributor.authorW. T. B. Uther
dc.date.accessioned2019-10-28T12:14:10Z
dc.date.available2019-10-28T12:14:10Z
dc.date.created2018-09-05 00:34
dc.date.issued2009-09-08
dc.identifieroai:CiteSeerX.psu:10.1.1.143.2080
dc.identifierhttp://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.2080
dc.identifier.urihttp://hdl.handle.net/20.500.12424/2458937
dc.description.abstractThis paper provides a study of probabilistic modelling, inference and learning in a logic-based setting. We show how probability densities, being functions, can be represented and reasoned with naturally and directly in higher-order logic, an expressive formalism not unlike the (informal) everyday language of mathematics. We give efficient inference algorithms and illustrate the general approach with a diverse collection of applications. Some learning issues are also considered.
dc.format.mediumapplication/pdf
dc.languageen
dc.language.isoeng
dc.rightsMetadata may be used without restrictions as long as the oai identifier remains attached to it.
dc.titleProbabilistic Modelling, Inference and Learning using Logical Theories
dc.typetext
ge.collectioncodeOAIDATA
ge.dataimportlabelOAI metadata object
ge.identifier.legacyglobethics:15105143
ge.identifier.permalinkhttps://www.globethics.net/gel/15105143
ge.lastmodificationdate2018-09-05 00:34
ge.lastmodificationuseradmin@pointsoftware.ch (import)
ge.submissions0
ge.oai.exportid149801
ge.oai.repositoryid54
ge.oai.streamid2
ge.setnameGlobeEthicsLib
ge.setspecglobeethicslib
ge.linkhttp://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.2080
ge.linkhttp://discus.anu.edu.au/~kee/prihol.pdf


This item appears in the following Collection(s)

Show simple item record