Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review

Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review (1996)

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by Mary Kathryn Cowles , Bradley P. Carlin

Venue:	Journal of the American Statistical Association
Citations:	161 - 5 self

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Datum	Value	Source
TITLE	Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review	user correction - Legacy Corrections
AUTHOR NAME	Mary Kathryn Cowles	user correction - Legacy Corrections
AUTHOR AFFIL	1	user correction - Legacy Corrections
AUTHOR NAME	Bradley P. Carlin	user correction - Legacy Corrections
AUTHOR AFFIL	Divison of Biostatistics at the University of Minnesota and then Assistant Professor, Biostatistics Section,; Department of Preventive and Societal Medicine, University of Nebraska Medical Center, Omaha	user correction - Legacy Corrections
AUTHOR ADDR	NE 68198. The	user correction - Legacy Corrections
ABSTRACT	A critical issue for users of Markov Chain Monte Carlo (MCMC) methods in applications is how to determine when it is safe to stop sampling and use the samples to estimate characteristics of the distribution of interest. Research into methods of computing theoretical convergence bounds holds promise for the future but currently has yielded relatively little that is of practical use in applied work. Consequently, most MCMC users address the convergence problem by applying diagnostic tools to the output produced by running their samplers. After giving a brief overview of the area, we provide an expository review of thirteen convergence diagnostics, describing the theoretical basis and practical implementation of each. We then compare their performance in two simple models and conclude that all the methods can fail to detect the sorts of convergence failure they were designed to identify. We thus recommend a combination of strategies aimed at evaluating and accelerating MCMC sampler conver...	user correction - Legacy Corrections
YEAR	1996	INFERENCE
VENUE	Journal of the American Statistical Association	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	883--904	INFERENCE
VOLUME	91	INFERENCE
CITATIONS	56 found	ParsCit 1.0