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On Markov chain Monte Carlo acceleration

Gelfand, Alan E. and Sahu, Sujit K. (1994) On Markov chain Monte Carlo acceleration Journal of Computational and Graphical Statistics, 3, (3), pp. 261-276.

Record type: Article


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


Local EPrints ID: 54072
ISSN: 1061-8600
PURE UUID: d6c426ab-c555-4420-bc39-329ab6b9535e
ORCID for Sujit K. Sahu: ORCID iD

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Date deposited: 05 Aug 2008
Last modified: 17 Jul 2017 14:36

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Author: Alan E. Gelfand
Author: Sujit K. Sahu ORCID iD

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