Arbeitspapier
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Discussion Paper ; No. 489
- Thema
-
Imprecise Dirichlet Model
imprecise probabilities
interval probability
known number of categories
lower and upper probabilities
multinominal data
nonparametric predictive inference
probability wheel
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Coolen, F. P. A.
Augustin, Thomas
- Ereignis
-
Veröffentlichung
- (wer)
-
Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
- (wo)
-
München
- (wann)
-
2006
- DOI
-
doi:10.5282/ubm/epub.1857
- Handle
- URN
-
urn:nbn:de:bvb:19-epub-1857-4
- Letzte Aktualisierung
-
10.03.2025, 11:41 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
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Objekttyp
- Arbeitspapier
Beteiligte
- Coolen, F. P. A.
- Augustin, Thomas
- Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
Entstanden
- 2006
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