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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