Abstract
We address the problem to estimate a Kronecker graphical model corresponding to an autoregressive and moving average (ARMA) stochastic process. The graph describes conditional dependence relations among the components of the process. We propose a Bayesian approach to estimate the model and the topology. We test the effectiveness of the proposed method by some numerical experiments.
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