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Convergence of conditional Metropolis-Hastings samplers

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Jones, Galin L., Roberts, Gareth O. and Rosenthal, Jeffrey S. (Jeffrey Seth) (2014) Convergence of conditional Metropolis-Hastings samplers. Advances in Applied Probability, Volume 46 (Number 2). pp. 422-445. doi:10.1239/aap/1401369701

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Official URL: http://dx.doi.org/10.1239/aap/1401369701

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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.

Item Type: Journal Article
Divisions: Faculty of Science > Statistics
Journal or Publication Title: Advances in Applied Probability
Publisher: Applied Probability Trust
ISSN: 0001-8678
Official Date: 29 May 2014
Dates:
DateEvent
29 May 2014Published
Volume: Volume 46
Number: Number 2
Page Range: pp. 422-445
DOI: 10.1239/aap/1401369701
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access

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