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.
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
|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
|Date Deposited:||05 Aug 2008|
|Last Modified:||02 Mar 2012 12:50|
|Contact Email Address:||S.K.Sahu@soton.ac.uk|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
Actions (login required)