Empirical Bayesian Learning in AR Graphical Models

2019/07/08 by Zorzi, Mattia
#FOS: Computer and information sciences #FOS: Mathematics #Methodology (stat.ME) #Optimization and Control (math.OC)

paper · doi:10.48550/arxiv.1907.03829

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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