Adaptive MCMC and model selection

Document Type

Presentation Abstract

Presentation Date

3-9-2009

Abstract

Several adaptive Markov chain Monte Carlo (MCMC) methods for doing Bayesian statistical inference have been developed recently. These adaptive methods aim to increase the efficiency of the Metropolis-Hastings sampling algorithm and to allow automatic tuning of the algorithm's proposal distribution. This is useful especially in estimation problems in high dimensional and computationally intensive models. Adaptive methods are also good building blocks for implementation of general MCMC software as they need very little user intervention compared to the non-adaptive versions. Theoretical considerations are needed to ensure that the adaptive methods preserve the correct stationary distribution. Application examples are presented in the fields of environmental modeling and geophysical remote sensing. An extension to Adaptive Metropolis called adaptive automatic reversible jump MCMC (AARJ) allows for Bayesian model selection and averaging, and to incorporate model uncertainty in the statistical analysis of nonlinear models.

Additional Details

Monday, 9 March 2009
3:10 p.m. in Math 103
4:00 p.m. Refreshments in Math Lounge 109

This document is currently not available here.

Share

COinS