A nonparametric predictive alternative to the Imprecise Dirichlet Model: the case of a known number of categories
Beschreibung
vor 18 Jahren
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.
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.
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