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 sparse 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.

中文翻译：

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Kronecker图形模型的自回归识别

我们解决该问题，以估计与自回归高斯随机过程相对应的Kronecker图形模型。后者由功率谱密度函数完全描述，该函数的逆函数具有支持力，可以支持稀疏的Kronecker乘积分解。我们提出一种贝叶斯方法来估计这种模型。通过一些数值实验，我们测试了该方法的有效性。我们还将程序应用于城市污染监测数据。