Practical Regeneration for Markov Chain Monte Carlo Simulation

Regeneration is a useful tool in Markov chain Monte Carlo simulation, since it can be used to side-step the burn-in problem and to construct estimates of the variance of parameter estimates themselves. Unfortunately, it is often difficult to take advantage of, since for most chains, no recurrent atom exists, and it is not always easy to use Nummelin's splitting method to identify regeneration points. This paper describes a simple and practical method of obtaining regeneration in a Markov chain. The application of this method in simulation is discussed, and examples are given.