Convergence of conditional metropolis-hastings samplers

Galin L. Jones, Gareth O. Roberts, Jeffrey S. Rosenthal

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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 aCMHsampler used for drawing Bayesian inferences about the entire sample path of a diffusion process, based only upon discrete observations.

Original languageEnglish (US)
Pages (from-to)422-445
Number of pages24
JournalAdvances in Applied Probability
Issue number2
StatePublished - Jun 2014


  • Convergence rate
  • Geometric ergodicity
  • Gibbs sampler
  • Independence sampler
  • Markov chain Monte Carlo algorithm

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