  For the normal linear model variable selection problem, we propose selection
criteria based on a fully Bayes formulation with a generalization of Zellner's
$g$-prior which allows for $p>n$. A special case of the prior formulation is
seen to yield tractable closed forms for marginal densities and Bayes factors
which reveal new model evaluation characteristics of potential interest.
