Convergence of Conditional Metropolis-Hastings Samplers. (June 2014)

• Record Type: Journal Article
• Authors: Jones, Galin L.; Roberts, Gareth O.; Rosenthal, Jeffrey S.
• Is Part Of: Advances in applied probability. Volume 46:Number 2(2014)
• Page Start: 422
• Page End: 445
• Publication Date: 2014-06
• Subjects: Markov chain Monte Carlo algorithm, -- independence sampler, -- Gibbs sampler, -- geometric ergodicity, -- convergence rate; 60J05, -- 60J22, -- 65C40, -- 62F15; Probabilities -- Periodicals; Stochastic models -- Periodicals; Electronic journals; Periodicals; 519.2
• DOI: 10.1239/aap/1401369701
• Languages: English
• ISSNs: 0001-8678

Abstract

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.