JSM 2003 Online Program

Abstract #302385

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Activity Number:	310
Type:	Invited
Date/Time:	Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor:	Section on Statistical Computing
Abstract - #302385
Title:	Strategies for MCMC Acceleration
Author(s):	David Draper*+ and Shufeng Liu
Companies:	University of California and University of California, Santa Cruz
Address:	1156 High St., Santa Cruz, CA, 95064-1077,
Keywords:
Abstract:	Simulation-based MCMC methods for approximating high-dimensional integrals have sparked a revolution in inferential and predictive applications of Bayesian statistics in the last 15 years. The approach is extremely general, but the output of MCMC samplers often exhibits a high degree of positive autocorrelation, limiting the rate (per CPU second at which information about the posterior distribution of interest is obtained. We describe two strategies for achieving Monte Carlo acceleration in MCMC samplers and present some preliminary results.

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