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Overall Objective Priors

TLDR
In this paper, the authors consider three methods for selecting a single objective prior and study whether or not the resulting prior is a reasonable overall prior in a variety of problems including the multinomial problem.
Abstract
In multi-parameter models, reference priors typically depend on the parameter or quantity of interest, and it is well known that this is necessary to produce objective posterior distributions with optimal properties. There are, however, many situations where one is simultaneously interested in all the parameters of the model or, more realistically, in functions of them that include aspects such as prediction, and it would then be useful to have a single objective prior that could safely be used to produce reasonable posterior inferences for all the quantities of interest. In this paper, we consider three methods for selecting a single objective prior and study, in a variety of problems including the multinomial problem, whether or not the resulting prior is a reasonable overall prior.

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References
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Journal ArticleDOI

Pattern Recognition and Machine Learning

Radford M. Neal
- 01 Aug 2007 - 
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
Journal ArticleDOI

An invariant form for the prior probability in estimation problems.

TL;DR: It is shown that a certain differential form depending on the values of the parameters in a law of chance is invariant for all transformations of the parameter when the law is differentiable with regard to all parameters.
Journal ArticleDOI

Adaptive Rejection Sampling for Gibbs Sampling

TL;DR: In this paper, the authors proposed a method for rejection sampling from any univariate log-concave probability density function, which is adaptive: as sampling proceeds, the rejection envelope and the squeezing function converge to the density function.
Journal ArticleDOI

The Selection of Prior Distributions by Formal Rules

TL;DR: In this paper, a review of techniques for constructing non-informative priors is presented and some of the practical and philosophical issues that arise when they are used are discussed.
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