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.
- Language
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Englisch
- Bibliographic citation
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Series: Discussion Paper ; No. 489
- Subject
-
Imprecise Dirichlet Model
imprecise probabilities
interval probability
known number of categories
lower and upper probabilities
multinominal data
nonparametric predictive inference
probability wheel
- Event
-
Geistige Schöpfung
- (who)
-
Coolen, F. P. A.
Augustin, Thomas
- Event
-
Veröffentlichung
- (who)
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Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
- (where)
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München
- (when)
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2006
- DOI
-
doi:10.5282/ubm/epub.1857
- Handle
- URN
-
urn:nbn:de:bvb:19-epub-1857-4
- Last update
-
10.03.2025, 11:41 AM CET
Data provider
This object is provided by:
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Coolen, F. P. A.
- Augustin, Thomas
- Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
Time of origin
- 2006
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Number categories : dynamics, contact, typology
The number of response categories in ordered response models
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Modular functions and Dirichlet series in number theory, Bd. [2].
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Dirichlet.
Dirichlet Approximation and Universal Dirichlet Series
On estimating the number of people with known HIV positive status
(1,∞)-Categories and model categories
Dirichlet, Georg
Leieune-Dirichlet.