JSM 2004 - Toronto

Abstract #301697

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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 - #301697
Title: Intuitive, Easily Programmed Steps that Reduce MCMC Autocorrelation
Author(s): Todd L. Graves*+ and Paul L. Speckman and Dongchu Sun
Companies: Los Alamos National Laboratory and University of Missouri, Columbia and University of Missouri, Columbia
Address: MS F600, PO Box 1663, Los Alamos, NM, 87545,
Keywords: Metropolis algorithm ; YADAS ; Bayesian computation
Abstract:

Often, an analyst writes software to analyze a Bayesian model using Markov chain Monte Carlo, only to find that high correlations between parameters cause mixing to be so poor as to render the algorithm useless. We will demonstrate how augmenting existing algorithms with Metropolis(-Hastings) steps in well-chosen directions can dramatically improve mixing. These directions can usually be found through easy analysis of individual terms in the posterior distribution. The MCMC software package YADAS makes it particularly convenient to create algorithms featuring these steps.

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Revised March 2004