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arXiv · 2004.14199

Autoregressive Identification of Kronecker Graphical Models

Mattia Zorzi
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Abstract

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.

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Mattia Zorzi. 2020-04-29. Autoregressive Identification of Kronecker Graphical Models. https://arxiv.org/abs/2004.14199

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