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Gibbs Sampler Convergence Criteria

 

作者: Arnold Zellner,   Chung-Ki Min,  

 

期刊: Journal of the American Statistical Association  (Taylor Available online 1995)
卷期: Volume 90, issue 431  

页码: 921-927

 

ISSN:0162-1459

 

年代: 1995

 

DOI:10.1080/01621459.1995.10476591

 

出版商: Taylor & Francis Group

 

关键词: Bayesian analysis;Computational statistics;Numerical analysis;MCMC methods;Posterior distributions

 

数据来源: Taylor

 

摘要:

This article presents new operational convergence criteria for the Gibbs sampler (GS) and related procedures that are useful for determining whether they not only have converged but also have converged to provide reliable results. Three GS convergence criteria are presented and applied: the difference convergence criterion (DC2), the ratio convergence criterion (RC2), and the anchored ratio convergence criterion (ARC2). Their uses and properties are discussed and examples are analyzed to illustrate their application.

 

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