JSM 2004 Online Program

Abstract #301673

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Activity Number:	106
Type:	Topic Contributed
Date/Time:	Monday, August 9, 2004 : 10:30 AM to 12:20 PM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #301673
Title:	Characterizing Autocorrelation and Decorrelating Steps in MCMC Algorithms for Linear Models
Author(s):	Paul L. Speckman*+ and Todd L. Graves and Dongchu Sun
Companies:	University of Missouri, Columbia and Los Alamos National Laboratory and University of Missouri, Columbia
Address:	Dept. of Statistics, Columbia, MO, 65211,
Keywords:	MCMC ; mixing
Abstract:	Markov chain Monte Carlo algorithms for Bayesian analysis often suffer from poor mixing. This poor mixing is generally associated with high posterior correlations between parameters. In the case of Gaussian linear models with known variances, we characterize which functions of parameters mix poorly and demonstrate a practical way of removing all autocorrelations from the algorithms by adding extra Gibbs steps in suitable directions.

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