PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Practical Robust Estimators under the Imprecise Dirichlet Model
Marcus Hutter
International Journal of Approximate Reasoning Volume 50, Number 2, pp. 231-242, 2009.

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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:5851
Deposited By:Marcus Hutter
Deposited On:08 March 2010