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), 261-276.
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Description/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
| Item Type: | Article |
|---|---|
| ISSNs: | 1061-8600 (print) |
| Related URLs: | |
| 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 |
| Subjects: | Q Science > QA Mathematics |
| Divisions: | University Structure - Pre August 2011 > School of Mathematics > Statistics |
| Item ID: | 54072 |
| Date Deposited: | 05 Aug 2008 |
| Last Modified: | 02 Mar 2012 12:50 |
| Contributors: | Gelfand, Alan E. (Author) Sahu, Sujit K. (Author) |
| Date: | September 1994 |
| Status: | Published |
| Contact Email Address: | S.K.Sahu@soton.ac.uk |
| URI: | http://eprints.soton.ac.uk/id/eprint/54072 |
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