Analyzing Markov chain Monte Carlo output

Dootika Vats, Nathan Robertson, James M. Flegal, Galin L. Jones

Research output: Contribution to journalReview articlepeer-review

4 Scopus citations


Markov chain Monte Carlo (MCMC) is a sampling-based method for estimating features of probability distributions. MCMC methods produce a serially correlated, yet representative, sample from the desired distribution. As such it can be difficult to assess when the MCMC method is producing reliable results. We present some fundamental methods for ensuring a reliable simulation experiment. In particular, we present a workflow for output analysis in MCMC providing estimators, approximate sampling distributions, stopping rules, and visualization tools. This article is categorized under: Statistical Models > Bayesian Models Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC) Statistical and Graphical Methods of Data Analysis > Monte Carlo Methods.

Original languageEnglish (US)
Article numbere1501
JournalWiley Interdisciplinary Reviews: Computational Statistics
Issue number4
StatePublished - Jul 1 2020


  • Bayesian computation
  • Markov chain Monte Carlo
  • Monte Carlo
  • output analysis
  • stopping rules


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