Approximated Information Analysis in Bayesian Inference

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...

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書誌詳細

主要な著者:	Jung In Seo, Yongku Kim
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2015-03-01
シリーズ:	Entropy
主題:	Bayesian sensitivity Gibbs sampler Kullback–Leibler divergence Laplace approximation
オンライン･アクセス:	http://www.mdpi.com/1099-4300/17/3/1441
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