Efficient simulation of Bayesian logistic regression models

In this paper we highlight a data augmentation approach to
inference in the Bayesian logistic regression model. We demonstrate
that the resulting conditional likelihood of the regression
coefficients is multivariate normal, equivalent to a standard
Bayesian linear regression, which allows for efficient simulation
using a block Gibbs sampler. We illustrate that the method is
particularly suited to problems in covariate set uncertainty and
random effects models.