arXiv：cs.LG· Salha Mamane, Cheikh Tidiane Seck·· 4 小时前
Student Copula Markov Networks 论文：用 Copula 密度刻画无向概率图模型的成对马尔可夫性
Student Copula Markov Networks
AI 导读
该论文将概率图模型的适用范围扩展到非高斯数据，用 Copula 密度刻画无向概率图模型中的成对马尔可夫性，并据此推导出 Student copula Markov networks 成对马尔可夫性的一个必要条件。作者提出一种期望-条件最大化算法（ECM）用于带惩罚的最大似然估计与图选择，仿真研究显示其在估计模型参数和恢复底层图结构上有效。
正文
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.
| Subjects: | Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME) |
| MSC classes: | 62M40, 62H20, 68T05 |
| Cite as: | arXiv:2610.19135 [stat.ML] |
| (or arXiv:2610.19135v1 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19135 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Salha Mamane [view email]
[v1]
Tue, 13 Oct 2026 20:05:44 UTC (13 KB)
来源：arXiv：cs.LG · arxiv.org