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dc.contributor.authorCoolen, F. P. A.en
dc.contributor.authorAugustin, Thomasen
dc.date.accessioned2006-09-07-
dc.date.accessioned2010-05-14T10:14:37Z-
dc.date.available2010-05-14T10:14:37Z-
dc.date.issued2006-
dc.identifier.pidoi:10.5282/ubm/epub.1857en
dc.identifier.piurn:nbn:de:bvb:19-epub-1857-4en
dc.identifier.urihttp://hdl.handle.net/10419/31146-
dc.description.abstractNonparametric Predictive Inference (NPI) is a general methodology to learn from data in the absense of prior knowledge and without adding unjustified assumptions. This paper develops NPI for multinominal data where the total number of possible categories for the data is known. We present the general upper and lower probabilities and several of their properties. We also comment on differences between this NPI approach and corresponding inferences based on Walley's Imprecise Dirichlet Model.en
dc.language.isoengen
dc.publisher|aLudwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen |cMünchenen
dc.relation.ispartofseries|aDiscussion Paper |x489en
dc.subject.ddc519en
dc.subject.keywordImprecise Dirichlet Modelen
dc.subject.keywordimprecise probabilitiesen
dc.subject.keywordinterval probabilityen
dc.subject.keywordknown number of categoriesen
dc.subject.keywordlower and upper probabilitiesen
dc.subject.keywordmultinominal dataen
dc.subject.keywordnonparametric predictive inferenceen
dc.subject.keywordprobability wheelen
dc.titleA nonparametric predictive alternative to the Imprecise Dirichlet Model: the case of a known number of categories-
dc.type|aWorking Paperen
dc.identifier.ppn51717927Xen
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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