Translator Disclaimer
June 2014 Convergence of conditional Metropolis-Hastings samplers
Galin L. Jones, Gareth O. Roberts, Jeffrey S. Rosenthal
Author Affiliations +
Adv. in Appl. Probab. 46(2): 422-445 (June 2014). DOI: 10.1239/aap/1401369701

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.

Citation

Download Citation

Galin L. Jones. Gareth O. Roberts. Jeffrey S. Rosenthal. "Convergence of conditional Metropolis-Hastings samplers." Adv. in Appl. Probab. 46 (2) 422 - 445, June 2014. https://doi.org/10.1239/aap/1401369701

Information

Published: June 2014
First available in Project Euclid: 29 May 2014

zbMATH: 1379.60082
MathSciNet: MR3215540
Digital Object Identifier: 10.1239/aap/1401369701

Subjects:
Primary: 60J05, 60J22, 62F15, 65C40

Rights: Copyright © 2014 Applied Probability Trust

JOURNAL ARTICLE
24 PAGES

This article is only available to subscribers.
It is not available for individual sale.
+ SAVE TO MY LIBRARY

SHARE
Vol.46 • No. 2 • June 2014
Back to Top