  We address the problem to estimate a Kronecker graphical model corresponding
to an autoregressive Gaussian stochastic process. The latter is completely
described by the power spectral density function whose inverse has support
which admits a Kronecker product decomposition. We propose a Bayesian approach
to estimate such a model. We test the effectiveness of the proposed method by
some numerical experiments. We also apply the procedure to urban pollution
monitoring data.
