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 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

ISIPTA'07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications

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Nonparametric predictive inference in statistical process control

New methods for statistical process control are presented, where the inferences have a nonparametric predictive nature. We consider several problems in process control in terms of uncertainties about future observable random quantities, and we develop inferences for these random quantities hased on data available in the form of a reference set. We use Hill's assumption A(n), which enables predietive inferenee while adding only few assumptions to the data observed