Abstract #300884

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JSM 2003 Abstract #300884
Activity Number: 137
Type: Luncheons
Date/Time: Monday, August 4, 2003 : 12:30 PM to 1:50 PM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #300884
Title: Objective Bayes Variable Selection and StoCom
Author(s): Merlise A. Clyde*+
Companies: Duke University
Address: PO Box 90251, Durham, NC, 27708-0251,
Keywords: model selection ; prior distributions ; regression ; stochastic computation
Abstract:

With the advent of MCMC methodology and high-speed computing and our ability to stochastically search millions of models and assign to each a posterior probability, Bayesian variable selection has become a reality. A major difficulty has been elicitation of prior distributions for parameters within models and probabilities on models, with a growing need for default objective priors that perform well in a wide variety of settings. I describe the recommendations of the model selection working group, part of the year-long Stochastic Computation program at SAMSI, with highlights on new objective prior distributions and software.

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Revised March 2003