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

Year: 2025

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2511.14123

Authors: Roach, Lyndsay, Li, Qiong, Wang, Nanwei, Gao, Xin

Institutions: York University, Beijing Normal-Hong Kong Baptist University, Hong Kong Baptist University, University of New Brunswick

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7106097739

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