Title

stable GR: A stable Gelman-Rubin diagnostic for Markov chain Monte Carlo (R software package)

Department/School

Mathematics

Date

6-1-2018

Document Type

Article

Abstract

Practitioners of Bayesian statistics often use Markov chain Monte Carlo (MCMC) samplers to sample from a posterior distribution. This package determines whether the MCMC sample is large enough to yield reliable estimates of the target distribution. In particular, this calculates a Gelman-Rubin convergence diagnostic using stable and consistent estimators of Monte Carlo variance. Additionally, this uses the connection between an MCMC sample's effective sample size and the Gelman-Rubin diagnostic to produce a threshold for terminating MCMC simulation. Finally, this informs the user whether enough samples have been collected and (if necessary) estimates the number of samples needed for a desired level of accuracy. The theory underlying these methods can be found in "Revisiting the Gelman-Rubin Diagnostic" by Vats and Knudson (2018). (v. 1.0).

Citation/Other Information

Knudson, C. (2018). stable GR: A stable Gelman-Rubin diagnostic for Markov chain Monte Carlo (R software package). https://cran.r-project.org/package=stableGR.

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