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ISSN: 2168-9679

Journal of Applied & Computational Mathematics
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Research Article

Reflecting About Selecting Noninformative Priors

Kamary K1* and Robert CP2

1CEREMADE, Universite Paris-Dauphine, 75775 Paris cedex 16, France

2CREST-Insee and CEREMADE, Universite Paris-Dauphine, 75775 Paris cedex 16, France

*Corresponding Author:
Kaniav Kamary
CEREMADE, Universite Paris- Dauphine
75775 Paris cedex 16, France
Tel: 33144054405
E-mail:[email protected]

Received July 04, 2014; Accepted July 18, 2014; Published July 21, 2014

Citation: Kamary K, Robert CP (2014) Reflecting About Selecting Noninformative Priors. J Appl Computat Math 3: 175. doi: 10.4172/2168-9679.1000175

Copyright: © 2014 Kamary K, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Following the critical review of Seaman et al., we react on an essential aspect of Bayesian statistics, namely the selection of a prior density. In some cases, Bayesian data analysis remains stable under different choices of noninformative prior distributions. However, as discussed by Seaman et al., there may also be unintended consequences of a choice of noninformative prior and, according to these authors, this is a problem often ignored in applications of Bayesian inference". They focused on four examples, analyzing each for several choices of prior. Here, we reassess these examples and their Bayesian processing via different prior choices for fixed data sets. The conclusion is to infer the overall stability of the posterior distributions and to consider that the effect of reasonable noninformative priors is mostly negligible.

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