High-Dimensional Covariate-Dependent Discrete Graphical Models and Dynamic Ising Models

Lyndsay Roach; Qiong Li; Nanwei Wang; Xin Gao

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arXiv · 2511.14123

High-Dimensional Covariate-Dependent Discrete Graphical Models and Dynamic Ising Models

Lyndsay Roach·Qiong Li·Nanwei Wang·Xin Gao

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Abstract

We propose a covariate-dependent discrete graphical model for capturing dynamic networks among discrete random variables, allowing the dependence structure among vertices to vary with covariates. This discrete dynamic network encompasses the dynamic Ising model as a special case. We formulate a likelihood-based approach for parameter estimation and statistical inference. We achieve efficient parameter estimation in high-dimensional settings through the use of the pseudo-likelihood method. To perform model selection, a birth-and-death Markov chain Monte Carlo algorithm is proposed to explore the model space and select the most suitable model.

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BibTeX RIS

Lyndsay Roach, Qiong Li, Nanwei Wang, Xin Gao. 2025-11-18. High-Dimensional Covariate-Dependent Discrete Graphical Models and Dynamic Ising Models. https://arxiv.org/abs/2511.14123

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