When a number of distinct models contend for use in prediction, the choice of a single model
can offer rather unstable predictions. In regression, stochastic search variable selection with …

We consider the problem of accounting for model uncertainty in linear regression models.
Conditioning on a single selected model ignores model uncertainty, and thus leads to the …

In regression models, such as generalized linear models, there is often substantial prior
uncertainty about the choice of covariates to include. Conceptually, the Bayesian paradigm …

This paper focuses on the problem of variable selection in linear regression models. I briefly
review the method of Bayesian model averaging, which has become an important tool in …

We consider the problems of variable selection and accounting for model uncertainty in
linear regression models. Conditioning on a single selected model ignores model …

In this paper, we briefly review the main methodological aspects concerned with the
application of the Bayesian approach to model choice and model averaging in the context of …

Selecting a subset of variables for linear models remains an active area of research. This
article reviews many of the recent contributions to the Bayesian model selection and …

This article describes methods for efficient posterior simulation for Bayesian variable
selection in generalized linear models with many regressors but few observations. The …

In Chapter 12, we considered inference in a normal linear regression model with q
predictors. In many instances, the set of predictor variables X can be quite large, as one …

For the problem of model choice in linear regression, we introduce a Bayesian adaptive
sampling algorithm (BAS), that samples models without replacement from the space of …