Journal Article

Markov Chain Monte Carlo in Practice: A Roundtable Discussion

Robert E. Kass, Bradley P. Carlin, Andrew Gelman and Radford M. Neal
The American Statistician
Vol. 52, No. 2 (May, 1998), pp. 93-100
DOI: 10.2307/2685466
Stable URL: http:/stable/2685466
Page Count: 8
Were these topics helpful?
See something inaccurate? Let us know!

Select the topics that are inaccurate.

  • Download ($14.00)
  • Add to My Lists
  • Cite this Item
Markov Chain Monte Carlo in Practice: A Roundtable Discussion
Preview not available

Abstract

Markov chain Monte Carlo (MCMC) methods make possible the use of flexible Bayesian models that would otherwise be computationally infeasible. In recent years, a great variety of such applications have been described in the literature. Applied statisticians who are new to these methods may have several questions and concerns, however: How much effort and expertise are needed to design and use a Markov chain sampler? How much confidence can one have in the answers that MCMC produces? How does the use of MCMC affect the rest of the model-building process? At the Joint Statistical Meetings in August, 1996, a panel of experienced MCMC users discussed these and other issues, as well as various "tricks of the trade". This article is an edited recreation of that discussion. Its purpose is to offer advice and guidance to novice users of MCMC-and to not-so-novice users as well. Topics include building confidence in simulation results, methods for speeding and assessing convergence, estimating standard errors, identification of models for which good MCMC algorithms exist, and the current state of software development.

Page Thumbnails

  • Thumbnail: Page 
93
    93
  • Thumbnail: Page 
94
    94
  • Thumbnail: Page 
95
    95
  • Thumbnail: Page 
96
    96
  • Thumbnail: Page 
97
    97
  • Thumbnail: Page 
98
    98
  • Thumbnail: Page 
99
    99
  • Thumbnail: Page 
100
    100