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Entropy 2015, 17(3), 1441-1451; doi:10.3390/e17031441

Approximated Information Analysis in Bayesian Inference

1
Department of Statistics, Yeungnam University, Gyeongsan 712-749, Korea
2
Department of Statistics, Kyungpook National University, Daegu 702-701, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Kevin H. Knuth
Received: 30 December 2014 / Revised: 15 March 2015 / Accepted: 19 March 2015 / Published: 20 March 2015
(This article belongs to the Section Information Theory)
View Full-Text   |   Download PDF [231 KB, uploaded 20 March 2015]

Abstract

In models with nuisance parameters, Bayesian procedures based on Markov Chain Monte Carlo (MCMC) methods have been developed to approximate the posterior distribution of the parameter of interest. Because these procedures require burdensome computations related to the use of MCMC, approximation and convergence in these procedures are important issues. In this paper, we explore Gibbs sensitivity by using an alternative to the full conditional distribution of the nuisance parameter. The approximate sensitivity of the posterior distribution of interest is studied in terms of an information measure, including Kullback–Leibler divergence. As an illustration, we then apply these results to simple spatial model settings. View Full-Text
Keywords: Bayesian sensitivity; Gibbs sampler; Kullback–Leibler divergence; Laplace approximation Bayesian sensitivity; Gibbs sampler; Kullback–Leibler divergence; Laplace approximation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Seo, J.I.; Kim, Y. Approximated Information Analysis in Bayesian Inference. Entropy 2015, 17, 1441-1451.

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