Gelman–Rubin convergence diagnostic using multiple chains
As of Stata 16, see [BAYES] bayesstats grubin and Bayesian analysis: Gelman-Rubin convergence diagnostic.
The original blog posted May 26, 2016, omitted option initrandom from the bayesmh command. The code and the text of the blog entry were updated on August 9, 2018, to reflect this.
Overview
MCMC algorithms used for simulating posterior distributions are indispensable tools in Bayesian analysis. A major consideration in MCMC simulations is that of convergence. Has the simulated Markov chain fully explored the target posterior distribution so far, or do we need longer simulations? A common approach in assessing MCMC convergence is based on running and analyzing the difference between multiple chains.
For a given Bayesian model, bayesmh is capable of producing multiple Markov chains with randomly dispersed initial values by using the initrandom option, available as of the update on 19 May 2016. In this post, I demonstrate the Gelman–Rubin diagnostic as a more formal test for convergence using multiple chains. For graphical diagnostics, see Graphical diagnostics using multiple chains in [BAYES] bayesmh for more details. To compute the Gelman–Rubin diagnostic, I use an unofficial command, grubin, which can be installed by typing the following in Stata: Read more…