Empirical Bayesian Learning in AR Graphical Models

Mattia Zorzi

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arXiv · 1907.03829

Empirical Bayesian Learning in AR Graphical Models

Mattia Zorzi

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Abstract

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.

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Mattia Zorzi. 2019-07-08. Empirical Bayesian Learning in AR Graphical Models. https://arxiv.org/abs/1907.03829

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