.png){kind=icon}
Return
Practical robust estimators for the imprecise Dirichlet model
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijar.2008.03.020.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
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
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3
Papers:
3.0K
Citations:
5.1K
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSaveShareSave no more
no more