  We consider the problem of learning the structure of a high dimensional
precision matrix under sparsity assumptions. We propose to use a shrinkage
prior, called the DL-graphical prior based on the Dirichlet-Laplace prior used
for the Gaussian mean problem. A posterior sampling scheme based on Gibbs
sampling is also provided along with theoretical guarantees of the method by
obtaining the posterior convergence rate of the precision matrix.
