Gelfand, Alan E. and Sahu, Sujit K.
On Markov chain Monte Carlo acceleration.
Journal of Computational and Graphical Statistics, 3, (3), .
Full text not available from this repository.
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
||Q Science > QA Mathematics
||University Structure - Pre August 2011 > School of Mathematics > Statistics
|Accepted Date and Publication Date:
||05 Aug 2008
||31 Mar 2016 12:33
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