A Coupling-Regeneration Scheme for Diagnosing Convergence in Markov Chain Monte Carlo Algorithms
نویسنده
چکیده
I propose a convergence diagnostic for Markov chain Monte Carlo (MCMC) algorithms based on couplings of a Markov chain with an auxiliary chain that is periodically restarted from a xed parameter value. The diagnostic provides a mechanism for estimating the spe-ciic constants governing the rate of convergence of geometrically and uniformly ergodic chains, and provides a lower bound on the eeective sample size of a MCMC run. It also provides a simple procedure for obtaining what is, with high probability, an independent sample from the stationary distribution.
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