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Gibbs Sampling with Diffuse Proper Priors: A Valid Approach to Data-Driven Inference?

Gibbs Sampling with Diffuse Proper Priors: A Valid Approach to Data-Driven Inference? Abstract This article demonstrates by example that the use of the Gibbs sampler with diffuse proper priors can lead to inaccurate posterior estimates. Our results show that such inaccuracies are not merely limited to small sample settings. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Computational and Graphical Statistics Taylor & Francis

Gibbs Sampling with Diffuse Proper Priors: A Valid Approach to Data-Driven Inference?

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References (13)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1537-2715
eISSN
1061-8600
DOI
10.1080/10618600.1998.10474776
Publisher site
See Article on Publisher Site

Abstract

Abstract This article demonstrates by example that the use of the Gibbs sampler with diffuse proper priors can lead to inaccurate posterior estimates. Our results show that such inaccuracies are not merely limited to small sample settings.

Journal

Journal of Computational and Graphical StatisticsTaylor & Francis

Published: Sep 1, 1998

Keywords: Diffuse priors; Impropriety; Maximum likelihood estimation; Noninformative priors; Probit-normal

There are no references for this article.