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Student Copula Markov Networks

  • Salha Mamane
  • Cheikh Tidiane Seck
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Abstract

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.

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Cite

BibTeXRIS
BibTeX
@misc{mamane2026student,
  title = {{Student Copula Markov Networks}},
  author = {Salha Mamane and Cheikh Tidiane Seck},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.19135},
  eprint = {2610.19135},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.19135},
}
RIS
TY  - UNPB
TI  - Student Copula Markov Networks
AU  - Salha Mamane
AU  - Cheikh Tidiane Seck
PY  - 2026
DA  - 2026/10/13
PB  - arXiv
DO  - 10.48550/arxiv.2610.19135
UR  - https://arxiv.org/abs/2610.19135
AB  - 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.
ER  -