# Student Copula Markov Networks

arXiv: https://arxiv.org/abs/2610.19135 · HTML: https://mlsanity.com/paper/2610.19135/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 13 Oct 2026.

## Builds on (most cited first, 4 of 4)

- [Coordinate Descent Algorithms](https://mlsanity.com/paper/1502.04759/) (2015)
- [A Primer on Coordinate Descent Algorithms](https://mlsanity.com/paper/1610.00040/) (2016)
- [Robust Graphical Modeling with t-Distributions](https://mlsanity.com/paper/1408.2033/) (2014)
- [Copula Graphical Models for Heterogeneous Mixed Data](https://mlsanity.com/paper/2210.13140/) (2022)

## Abstract (arXiv)

This paper extends the applicability of probabilistic graphical models to non Gaussian data. It establishes a characterization of the pairwise Markov property in undirected probabilistic graphical models in terms of copula densities. Building on this characterization, we derive a necessary condition for the pairwise Markov property in Student copula Markov networks. An Expectation-Conditional Maximization algorithm (ECM) is provided to facilitate the penalized maximum likelihood estimation and graph selection. The performance of the proposed algorithm is evaluated through simulation studies, which demonstrate its effectiveness in estimating model parameters and recovering the underlying graph structure.