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Practical robust estimators for the imprecise Dirichlet model
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DOI：10.1016/j.ijar.2008.03.020.png)
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Abstract
En 中文
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. (C) 2008 Elsevier Inc. All rights reserved.
Keywords:
Imprecise Dirichlet model
Exact
Conservative
Approximate
Robust
Credible interval estimates
Entropy
Mutual information
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