JSM 2004 Online Program

Abstract #301107

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Activity Number:	46
Type:	Topic Contributed
Date/Time:	Sunday, August 8, 2004 : 4:00 PM to 5:50 PM
Sponsor:	Section on Statistical Computing
Abstract - #301107
Title:	Bayesian Sample Sizes for Design and Inference
Author(s):	Bertrand S. Clarke*+
Companies:	University of British Columbia/SAMSI
Address:	1808 White Pine Dr., Durham, NC, 27705,
Keywords:	sample size ; asymptotics ; posterior
Abstract:	Bayesian statistical inference is often based on the posterior density. We use asymptotic expressions for the expected value, under a fixed parameter and under the marginal for the data, of certain functionals of a posterior. The generality of our approach permits us to do sample size calculations and to evaluate the effective size of a sample in terms of independent data. Our approach is not tied to specific parametric families and does not rely on computationally intensive simulation methods. This is joint work with several collaborators.

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