On Nonparametric Predictive Inference and Objective Bayesianism
Journal of Logic, Language and Information
Decision making under incomplete data using the imprecise Dirichlet model
International Journal of Approximate Reasoning
Representation insensitivity in immediate prediction under exchangeability
International Journal of Approximate Reasoning
Credible classification for environmental problems
Environmental Modelling & Software
Upper entropy of credal sets. Applications to credal classification
International Journal of Approximate Reasoning
An introduction to the imprecise Dirichlet model for multinomial data
International Journal of Approximate Reasoning
On nonparametric predictive inference for ordinal data
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Imprecise probabilities for representing ignorance about a parameter
International Journal of Approximate Reasoning
Expert Systems with Applications: An International Journal
Classification with decision trees from a nonparametric predictive inference perspective
Computational Statistics & Data Analysis
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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 when the total number of possible categories for the data is known. We present the upper and lower probabilities for events involving the next observation and several of their properties. We also comment on differences between this NPI approach and corresponding inferences based on Walley's Imprecise Dirichlet Model.