A nonparametric predictive alternative to the Imprecise Dirichlet Model: the case of a known number of categories - F. P. A. Coolen; Thomas Augustin (Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen, 2006)
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.
urn:nbn:de:bvb:19-epub-1857-4
Keywords: 519; Imprecise Dirichlet Model; imprecise probabilities; interval probability; known number of categories; lower and upper probabilities; multinominal data; nonparametric predictive inference; probability wheel