On Markov chain Monte Carlo acceleration
On Markov chain Monte Carlo acceleration
Markov chain Monte Carlo (MCMC) methods are currently enjoying a surge of interest within the statistical community. The goal of this work is to formalize and support two distinct adaptive strategies which typically accelerate the convergence of a MCMC algorithm. One approach is through resampling; the other incorporates adaptive switching of the transition kernel. Support is both by analytic arguments and simulation study. Application is envisioned in low dimensional but non-trivial problems. Two pathological illustrations are presented. Connections with reparametrization are discussed as well as possible difficulties with infinitely often adaptation
statistics and probability, Monte Carlo Method, Markov processes, algotiyhms, simulation, probability, density functions, convergence, adaptation, switching, transitions, kernel fucntions, strategy, numerical analysis, MCMC (Markov Chain Monte Carlo), Gibbs sampler
261-276
Gelfand, Alan E.
1dc59cf1-5e5f-4001-b1f9-92b0a8e2f64f
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
September 1994
Gelfand, Alan E.
1dc59cf1-5e5f-4001-b1f9-92b0a8e2f64f
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Gelfand, Alan E. and Sahu, Sujit K.
(1994)
On Markov chain Monte Carlo acceleration.
Journal of Computational and Graphical Statistics, 3 (3), .
Abstract
Markov chain Monte Carlo (MCMC) methods are currently enjoying a surge of interest within the statistical community. The goal of this work is to formalize and support two distinct adaptive strategies which typically accelerate the convergence of a MCMC algorithm. One approach is through resampling; the other incorporates adaptive switching of the transition kernel. Support is both by analytic arguments and simulation study. Application is envisioned in low dimensional but non-trivial problems. Two pathological illustrations are presented. Connections with reparametrization are discussed as well as possible difficulties with infinitely often adaptation
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Published date: September 1994
Keywords:
statistics and probability, Monte Carlo Method, Markov processes, algotiyhms, simulation, probability, density functions, convergence, adaptation, switching, transitions, kernel fucntions, strategy, numerical analysis, MCMC (Markov Chain Monte Carlo), Gibbs sampler
Organisations:
Statistics
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Local EPrints ID: 54072
URI: http://eprints.soton.ac.uk/id/eprint/54072
ISSN: 1061-8600
PURE UUID: d6c426ab-c555-4420-bc39-329ab6b9535e
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Date deposited: 05 Aug 2008
Last modified: 12 Dec 2021 03:11
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Author:
Alan E. Gelfand
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