Abstract #300501

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JSM 2003 Abstract #300501
Activity Number: 310
Type: Invited
Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #300501
Title: A Multiresolution GA-Style MCMC Algorithm
Author(s): Christopher H. Holloman*+ and Herbert K. H. Lee and David Higdon
Companies: Duke University and University of California, Santa Cruz and Los Alamos National Laboratory
Address: 2738 Quarry Lake Dr., Columbus, OH, 43204,
Keywords: Bayesian statistics ; MCMC ; genetic algorithms ; spatial models
Abstract:

Due to modern advances in computing power, the use of increasingly complex models has become practical. One class of large models that often relies on numerical techniques for parameter estimation is multiresolution spatial models. Unfortunately, due to the inherent multicolinearity in spatial modeling, numerical sampling techniques used to estimate parameters often explore the parameter space slowly resulting in unreliable or unstable estimates. We propose a multiresolution MCMC algorithm that incorporates elements of genetic algorithms to improve sampling in these sorts of complex models. These methods are demonstrated on an example from groundwater hydrology.

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