This Title All WIREs
How to cite this WIREs title:
WIREs Comp Stat

Analyzing Markov chain Monte Carlo output

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

Abstract 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
Trace plots of λ and β for an initial run of the Markov chain
[ Normal View | Magnified View ]
Marginal density plots with mean and 95% credible intervals marked. Simultaneous uncertainty bands are drawn, but are nearly indistinguishable from the estimates
[ Normal View | Magnified View ]
Autocorrelation and cross‐correlation plots of MTTF and R(1500). Also, a 95% confidence region for the Monte Carlo average of MTTF and R(1500)
[ Normal View | Magnified View ]

Browse by Topic

Statistical and Graphical Methods of Data Analysis > Monte Carlo Methods
Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC)
Statistical Models > Bayesian Models

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts