Convergence of Conditional Metropolis-Hastings Samplers. (June 2014) - Jones, Galin L.; Roberts, Gareth O.; Rosenthal, Jeffrey S.
Abstract : We consider Markov chain Monte Carlo algorithms which combine Gibbs updates with Metropolis-Hastings updates, resulting in a conditional Metropolis-Hastings sampler (CMH sampler). We develop conditions under which the CMH sampler will be geometrically or uniformly ergodic. We illustrate our results by analysing a CMH sampler used for drawing Bayesian inferences about the entire sample path of a diffusion process, based only upon discrete observations.