  We address the problem of learning graphical models which correspond to high
dimensional autoregressive stationary stochastic processes. A graphical model
describes the conditional dependence relations among the components of a
stochastic process and represents an important tool in many fields. We propose
an empirical Bayes estimator of sparse autoregressive graphical models and
latent-variable autoregressive graphical models. Numerical experiments show the
benefit to take this Bayesian perspective for learning these types of graphical
models.
