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dc.contributor.authorCoolen, F. P. A.en_US
dc.contributor.authorAugustin, Thomasen_US
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_US
dc.publisher|aTechn. Univ.; Sonderforschungsbereich 386, Statistische Analyse Diskreter Strukturen|cMünchenen_US
dc.relation.ispartofseries|aDiscussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München|x489en_US
dc.subject.keywordImprecise Dirichlet Modelen_US
dc.subject.keywordimprecise probabilitiesen_US
dc.subject.keywordinterval probabilityen_US
dc.subject.keywordknown number of categoriesen_US
dc.subject.keywordlower and upper probabilitiesen_US
dc.subject.keywordmultinominal dataen_US
dc.subject.keywordnonparametric predictive inferenceen_US
dc.subject.keywordprobability wheelen_US
dc.titleA nonparametric predictive alternative to the Imprecise Dirichlet Model: the case of a known number of categoriesen_US
dc.type|aWorking Paperen_US

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