# A Scalable Strategy for the Identification of Latent-variable Graphical Models

arXiv: https://arxiv.org/abs/1809.01608 · HTML: https://mlsanity.com/paper/1809.01608/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [Identification of Non-causal Graphical Models](https://mlsanity.com/paper/2410.09480/) (2024)

## Abstract (arXiv)

In this paper we propose an identification method for latent-variable graphical models associated to autoregressive (AR) Gaussian stationary processes. The identification procedure exploits the approximation of AR processes through stationary reciprocal processes thus benefiting of the numerical advantages of dealing with block-circulant matrices. These advantages become more and more significant as the order of the process gets large. We show how the identification can be cast in a regularized convex program and we present numerical examples that compares the performances of the proposed method with the existing ones.