# Autoregressive Identification of Kronecker Graphical Models

arXiv: https://arxiv.org/abs/2004.14199 · HTML: https://mlsanity.com/paper/2004.14199/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

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

- [Learning Sparse High-Dimensional Matrix-Valued Graphical Models From Dependent Data](https://mlsanity.com/paper/2404.19073/) (2024)
- [Identification of Non-causal Graphical Models](https://mlsanity.com/paper/2410.09480/) (2024)

## Abstract (arXiv)

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.