  Selecting a subset of variables for linear models remains an active area of
research. This paper reviews many of the recent contributions to the Bayesian
model selection and shrinkage prior literature. A posterior variable selection
summary is proposed, which distills a full posterior distribution over
regression coefficients into a sequence of sparse linear predictors.
