# AR Identification of Latent-variable Graphical Models

arXiv: https://arxiv.org/abs/1405.0027 · HTML: https://mlsanity.com/paper/1405.0027/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2014.

## Built on by (most cited first, 5 of 5)

- [Learning Sparse High-Dimensional Matrix-Valued Graphical Models From Dependent Data](https://mlsanity.com/paper/2404.19073/) (2024)
- [Graphical estimation of multivariate count time series](https://mlsanity.com/paper/2302.08801/) (2023)
- [Probabilistic Reduced-Dimensional Vector Autoregressive Modeling with Oblique Projections](https://mlsanity.com/paper/2401.07206/) (2024)
- [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)

The paper proposes an identification procedure for autoregressive gaussian stationary stochastic processes wherein the manifest (or observed) variables are mostly related through a limited number of latent (or hidden) variables. The method exploits the sparse plus low-rank decomposition of the inverse of the manifest spectral density and the efficient convex relaxations recently proposed for such decomposition.