  We consider the problem of learning graphical models where the support of the
concentration matrix can be decomposed as a Kronecker product. We propose a
method that uses the Bayesian hierarchical learning modeling approach. Thanks
to the particular structure of the graph, we use a the number of
hyperparameters which is small compared to the number of nodes in the graphical
model. In this way, we avoid overfitting in the estimation of the
hyperparameters. Finally, we test the effectiveness of the proposed method by a
numerical example.
