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Activity Number: 15
Type: Topic Contributed
Date/Time: Sunday, August 6, 2006 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #307365
Title: Using Incompatibility To Build Fast Gibbs Samplers
Author(s): Taeyoung Park*+ and David A. van Dyk
Companies: Harvard University and University of California, Irvine
Address: One Oxford Street, Cambridge, MA, 02138,
Keywords: Bayesian hierarchical modeling ; Gibbs sampler ; functional incompatibility ; incompatible Gibbs sampler ; partially marginalized Gibbs sampler ; spectral analysis
Abstract:

Increasing computational power continues to popularize statistical computing methods and tools at an ever-increasing rate. Among the popular, computationally intensive methods, the Gibbs sampler (Geman and Geman 1984) has been spotlighted because of its simplicity and power to fit highly structured models. Its sometimes slow convergence, however, has been a long-standing complaint, especially when the highly structured models are fitted. In this paper, we present partially marginalized Gibbs sampling strategies that improve the convergence characteristics of an ordinary Gibbs sampler by exploiting a set of functionally incompatible conditional distributions avoided in the construction of the ordinary Gibbs sampler. We illustrate these strategies by fitting a highly structured multi-level spectral model, which an ordinary Gibbs sampler fails to fit.


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