Practical robust estimators for the imprecise Dirichlet model

Authors:
Marcus Hutter
Affiliations:
RSISE @ ANU and SML @ NICTA, Canberra, ACT 0200, Australia
Venue:
International Journal of Approximate Reasoning
Year:
2009

Citing 2
Cited 1

Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,

Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Robust inference of trees

Annals of Mathematics and Artificial Intelligence

A minimum distance estimator in an imprecise probability model -- Computational aspects and applications

International Journal of Approximate Reasoning

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Abstract

Walley's imprecise Dirichlet model (IDM) for categorical i.i.d. data extends the classical Dirichlet model to a set of priors. It overcomes several fundamental problems which other approaches to uncertainty suffer from. Yet, to be useful in practice, one needs efficient ways for computing the imprecise=robust sets or intervals. The main objective of this work is to derive exact, conservative, and approximate, robust and credible interval estimates under the IDM for a large class of statistical estimators, including the entropy and mutual information.