A nonparametric predictive alternative to the Imprecise Dirichlet Model: the case of a known number of categories

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.

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Year of publication:	2006
Authors:	Coolen, F. P. A. ; Augustin, Thomas
Publisher:	München : Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
Subject:	Imprecise Dirichlet Model \| imprecise probabilities \| interval probability \| known number of categories \| lower and upper probabilities \| multinominal data \| nonparametric predictive inference \| probability wheel

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Series:	Discussion Paper ; 489
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	10.5282/ubm/epub.1857 [DOI] 51717927X [GVK] hdl:10419/31146 [Handle]
Source:	EconStor

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010266240