Autoregressive identification of Kronecker graphical models

被引：12
|
作者：
Zorzi, Mattia [1 ]
机构：
[1] Univ Padua, Dipartimento Ingn Informaz, Via Gradenigo 6-B, I-35131 Padua, Italy
来源：
AUTOMATICA | 2020年 / 119卷
关键词：
Sparsity and Kronecker product inducing priors; Empirical Bayesian learning; Convex relaxation; Convex optimization; RELATIVE ENTROPY; TIME; CONVERGENCE; SELECTION;
D O I：
10.1016/j.automatica.2020.109053
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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. (C) 2020 Elsevier Ltd. All rights reserved.
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页数：13
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