2008 Workshop on Bayesian Model Selection and Objective Methods

University Of Florida, Gainesville FL

Investigators

Abstract

Model selection in the frequentist setting is a well developed field. In the Bayesian framework, in principle, model selection is very simple. Prior probability distributions are used to describe the uncertainty surrounding all unknowns, including models being considered and the parameters for these models. After observing the data, the posterior distribution provides a coherent post data summary of the remaining uncertainty which is relevant for model selection. However, the practical implementation of this approach is not straightforward, and involves issues such as choice of priors, interpretability, and computational feasibility. In this workshop, twelve distinguished individuals who work in Bayesian model selection present their work in a number of different areas, including determination of good objective priors, assessment of various information criteria (AIC, BIC, RIC), methods of calculation of Bayes factors, and Markov chain Monte Carlo. A number of young researchers participate in the workshop and present their work in poster sessions. Variable selection is an important and pervasive problem in scientific and medical research. A few important variables are to be selected from many candidates and used for understanding, prediction and decision making. Historically, variable selection has been carried out in a frequentist setting. However, Bayesian approaches offer important advantages. In broad terms, they give a coherent way of dealing with the distribution of the future response of an individual for whom the predictor variables are now known. Recent advances in both computing power and statistical methodology have greatly enhanced the feasibility of Bayesian approaches to regression and variable selection. The workshop provides an excellent opportunity to discuss the many recent significant developments in Bayesian model selection and objective methods; to discuss what has been found to work and what does not; and to identify important problems and new research directions.