JSM 2005 - Toronto

Abstract #304300

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 325
Type: Contributed
Date/Time: Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract - #304300
Title: An Approximation to Posterior Densities for Sample-based Inference
Author(s): Murali Haran*+
Companies: The Pennsylvania State University
Address: 326 Thomas Building, University Park, PA, 16802, United States
Keywords: Markov chain Monte Carlo ; hierarchical model ; heavy-tailed distributions ; Laplace approximation
Abstract:

Hierarchical Bayes models often result in posterior distributions that present challenges to nonexpert users of Markov chain Monte Carlo (MCMC) methods. I describe some recent work in constructing accurate approximations to posterior distributions for a class of Bayesian hierarchical models. I show how these approximations can be used routinely in Metropolis-Hastings algorithms to produce samplers with desirable properties. I conclude with an application of my methods to data examples.

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Revised March 2005