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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... (Update)
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BibTeX entry: (Update)
Cowles, M. K. and Carlin, B. P. (1996) Markov Chain Monte Carlo convergence diagnostics: a comparative review. Journal of the American Statistical Association, 91, 883--904. http://citeseer.ist.psu.edu/cowles96markov.html More
@article{ cowles96markov,
author = "Mary Kathryn Cowles and Bradley P. Carlin",
title = "{Markov} Chain {Monte Carlo} Convergence Diagnostics: {A} Comparative Review",
journal = "Journal of the American Statistical Association",
volume = "91",
number = "434",
month = "????",
pages = "883--904",
year = "1996",
url = "citeseer.ist.psu.edu/cowles96markov.html" }
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