An important aspect of Bayesian model selection is how to deal with huge
model spaces, since exhaustive enumeration of all the models entertained is
unfeasible and inferences have to be based on the very small proportion of
models visited. This is the case for the variable selection problem, with a
moderate to large number of possible explanatory variables being considered in
this paper.