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Title:A nonparametric predictive alternative to the Imprecise Dirichlet Model: the case of a known number of categories PDF Logo
Authors:Coolen, F. P. A.
Augustin, Thomas
Issue Date:2006
Series/Report no.:Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 489
Abstract:Nonparametric 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.
Subjects:Imprecise Dirichlet Model
imprecise probabilities
interval probability
known number of categories
lower and upper probabilities
multinominal data
nonparametric predictive inference
probability wheel
Persistent Identifier of the first edition:urn:nbn:de:bvb:19-epub-1857-4
Document Type:Working Paper
Appears in Collections:Discussion papers, SFB 386, LMU München

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