  The paper develops Bernstein von Mises Theorem under hierarchical $g$ -priors
for linear regression models. The results are obtained both when the error
variance is known, and also when it is unknown. An inverse gamma prior is
attached to the error variance in the later case. The paper also demonstrates
some connection between the total variation and $\alpha$-divergence measures.
