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

Coolen, F. P. A. and Augustin, Thomas (2006): A nonparametric predictive alternative to the Imprecise Dirichlet Model: the case of a known number of categories. Collaborative Research Center 386, Discussion Paper 489

Abstract

Nonparametric Predictive Inference (NPI) is a general methodology to learn from data in the absence of prior knowledge and without adding unjustified assumptions. This paper develops NPI for multinomial 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.

• Item Type: Paper
• Keywords: Imprecise Dirichlet Model, imprecise probabilities, interval probability, known number of categories, lower and upper probabilities, multinomial data, nonparametric predictive inference, probability wheel
• Faculties: Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386; Special Research Fields > Special Research Field 386
• Subjects: 500 Science > 510 Mathematics
• Language: English