# Empirical Bayesian Learning in AR Graphical Models

arXiv: https://arxiv.org/abs/1907.03829 · HTML: https://mlsanity.com/paper/1907.03829/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2019.

## Built on by (most cited first, 2 of 2)

- [Identification of Non-causal Graphical Models](https://mlsanity.com/paper/2410.09480/) (2024)
- [Learning Networks from Wide-Sense Stationary Stochastic Processes](https://mlsanity.com/paper/2412.03768/) (2024)

## Abstract (arXiv)

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.