Benchmark estimation for Markov chain Monte Carlo samples.

While studying various features of the posterior distribution of a vector-valued parameter using an MCMC sample, a subsample is repeatedly all that is available for analysis. The goal of benchmark estimation is to use the best available information, that is, the filled MCMC sample, to improve subsequent time estimates made on the basis of the subsample. We discuss a simple approach to do this and provide a theoretical basis for the [i]modus operandi[/i] The methodology and benefits of benchmark estimation are illustrated using a well-known example from the literature. We obtain nearly a 90% reduction in MSE with the technique based upon ...