High-Dimensional Covariate-Dependent Discrete Graphical Models and Dynamic Ising Models
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912716119080960 |
|---|---|
| author | Roach, Lyndsay Li, Qiong Wang, Nanwei Gao, Xin |
| author_facet | Roach, Lyndsay Li, Qiong Wang, Nanwei Gao, Xin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14123 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High-Dimensional Covariate-Dependent Discrete Graphical Models and Dynamic Ising Models Roach, Lyndsay Li, Qiong Wang, Nanwei Gao, Xin Methodology Statistics Theory 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. |
| title | High-Dimensional Covariate-Dependent Discrete Graphical Models and Dynamic Ising Models |
| topic | Methodology Statistics Theory |
| url | https://arxiv.org/abs/2511.14123 |