On the applicability of regenerative simulation in Markov chain Monte Carlo

James P. Hobert, Galin L. Jones, Brett Presnell, Jeffrey S. Rosenthal

Research output: Contribution to journalArticlepeer-review

67 Scopus citations


We consider the central limit theorem and the calculation of asymptotic standard errors for the ergodic averages constructed in Markov chain Monte Carlo. Chan & Geyer (1994) established a central limit theorem for ergodic averages by assuming that the underlying Markov chain is geometrically ergodic and that a simple moment condition is satisfied. While it is relatively straightforward to check Chan & Geyer's conditions, their theorem does not lead to a consistent and easily computed estimate of the variance of the asymptotic normal distribution. Conversely, Mykland et al. (1995) discuss the use of regeneration to establish an alternative central limit theorem with the advantage that a simple, consistent estimator of the asymptotic variance is readily available. However, their result assumes a pair of unwieldy moment conditions whose verification is difficult in practice. In this paper, we show that the conditions of Chan & Geyer's theorem are sufficient to establish the central limit theorem of Mykland et al. This result, in conjunction with other recent developments, should pave the way for more widespread use of the regenerative method in Markov chain Monte Carlo. Our results are illustrated in the context of the slice sampler.

Original languageEnglish (US)
Pages (from-to)731-743
Number of pages13
Issue number4
StatePublished - 2002

Bibliographical note

Funding Information:
The authors are grateful to Charlie Geyer for some helpful conversations and to an anonymous referee for several insightful comments, one of which led to Remark 6. This research was partially supported by grants from the U.S. National Science Foundation and the Natural Sciences and Engineering Research Council of Canada.


  • Asymptotic standard error
  • Burn-in
  • Central limit theorem
  • Geometric ergodicity
  • Minorisation condition
  • Slice sampler


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