# Structure learning of undirected graphical models for count data

arXiv: https://arxiv.org/abs/1810.10854 · HTML: https://mlsanity.com/paper/1810.10854/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2018.

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

- [Graphical estimation of multivariate count time series](https://mlsanity.com/paper/2302.08801/) (2023)

## Abstract (arXiv)

Biological processes underlying the basic functions of a cell involve complex interactions between genes. From a technical point of view, these interactions can be represented through a graph where genes and their connections are, respectively, nodes and edges. The main objective of this paper is to develop a statistical framework for modelling the interactions between genes when the activity of genes is measured on a discrete scale. In detail, we define a new algorithm for learning the structure of undirected graphs, PC-LPGM, proving its theoretical consistence in the limit of infinite observations. The proposed algorithm shows promising results when applied to simulated data as well as to real data.