Skip to main content
Article thumbnail
Location of Repository

Convergence of Conditional Metropolis-Hastings Samplers

By et al. Galin L. Jones

Abstract

We consider Markov chain Monte Carlo algorithms which combine Gibbs updates with Metropolis-Hastings updates, resulting in a conditional Metropolis-Hastings sampler (CMH). We develop conditions under which the CMH will be geometrically or uniformly ergodic. We illustrate our results by analysing a CMH used for drawing Bayesian inferences about the entire sample path of a diffusion process, based only upon discrete observations

Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.307.8981
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www.probability.ca/jeff... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.