Practical Robust Estimators for the Imprecise Dirichlet Model

Marcus Hutter

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arXiv · 0901.4137

Practical Robust Estimators for the Imprecise Dirichlet Model

Marcus Hutter

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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.

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BibTeX RIS

Marcus Hutter. 2009-01-26. Practical Robust Estimators for the Imprecise Dirichlet Model. https://arxiv.org/abs/0901.4137

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