# A Survey on Latent Tree Models and Applications

arXiv: https://arxiv.org/abs/1402.0577 · HTML: https://mlsanity.com/paper/1402.0577/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2014.

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

- [Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables](https://mlsanity.com/paper/2308.06718/) (2023)
- [Optimal estimation of Gaussian (poly)trees](https://mlsanity.com/paper/2402.06380/) (2024)
- [Modality Matching Matters: Calibrating Language Distances for Cross-Lingual Transfer in URIEL+](https://mlsanity.com/paper/2510.19217/) (2025)
- [Spectral Top-Down Recovery of Latent Tree Models](https://mlsanity.com/paper/2102.13276/) (2021)
- [EM's Convergence in Gaussian Latent Tree Models](https://mlsanity.com/paper/2211.11904/) (2022)

## Abstract (arXiv)

In data analysis, latent variables play a central role because they help provide powerful insights into a wide variety of phenomena, ranging from biological to human sciences. The latent tree model, a particular type of probabilistic graphical models, deserves attention. Its simple structure - a tree - allows simple and efficient inference, while its latent variables capture complex relationships. In the past decade, the latent tree model has been subject to significant theoretical and methodological developments. In this review, we propose a comprehensive study of this model. First we summarize key ideas underlying the model. Second we explain how it can be efficiently learned from data. Third we illustrate its use within three types of applications: latent structure discovery, multidimensional clustering, and probabilistic inference. Finally, we conclude and give promising directions for future researches in this field.