# Empirical Bayesian Learning in AR Graphical Models

## TL;DR

This paper introduces an empirical Bayes approach for learning sparse autoregressive graphical models, demonstrating its effectiveness through numerical experiments in high-dimensional settings.

## Contribution

It proposes a novel empirical Bayes estimator for sparse and latent-variable autoregressive graphical models, advancing Bayesian methods in this domain.

## Key findings

- Effective in high-dimensional settings
- Improves learning of sparse graphical models
- Numerical experiments confirm benefits of Bayesian approach

## 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.