A2 Copula-Driven Spatial Bayesian Neural Network For Modeling Non-Gaussian Dependence: A Simulation Study
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910975045664768 |
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| author | Aich, Agnideep Hewage, Sameera Murshed, Md Monzur Aich, Ashit Baran Mayeaux, Amanda Dey, Asim K. Das, Kumer P. Wade, Bruce |
| author_facet | Aich, Agnideep Hewage, Sameera Murshed, Md Monzur Aich, Ashit Baran Mayeaux, Amanda Dey, Asim K. Das, Kumer P. Wade, Bruce |
| contents | In this paper, we introduce the A2 Copula Spatial Bayesian Neural Network (A2-SBNN), a predictive spatial model designed to map coordinates to continuous fields while capturing both typical spatial patterns and extreme dependencies. By embedding the dual-tail novel Archimedean copula viz. A2 directly into the network's weight initialization, A2-SBNN naturally models complex spatial relationships, including rare co-movements in the data. The model is trained through a calibration-driven process combining Wasserstein loss, moment matching, and correlation penalties to refine predictions and manage uncertainty. Simulation results show that A2-SBNN consistently delivers high accuracy across a wide range of dependency strengths, offering a new, effective solution for spatial data modeling beyond traditional Gaussian-based approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24006 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A2 Copula-Driven Spatial Bayesian Neural Network For Modeling Non-Gaussian Dependence: A Simulation Study Aich, Agnideep Hewage, Sameera Murshed, Md Monzur Aich, Ashit Baran Mayeaux, Amanda Dey, Asim K. Das, Kumer P. Wade, Bruce Methodology Machine Learning 62H12, 62P10, 65C20, 62F15, 68T07 In this paper, we introduce the A2 Copula Spatial Bayesian Neural Network (A2-SBNN), a predictive spatial model designed to map coordinates to continuous fields while capturing both typical spatial patterns and extreme dependencies. By embedding the dual-tail novel Archimedean copula viz. A2 directly into the network's weight initialization, A2-SBNN naturally models complex spatial relationships, including rare co-movements in the data. The model is trained through a calibration-driven process combining Wasserstein loss, moment matching, and correlation penalties to refine predictions and manage uncertainty. Simulation results show that A2-SBNN consistently delivers high accuracy across a wide range of dependency strengths, offering a new, effective solution for spatial data modeling beyond traditional Gaussian-based approaches. |
| title | A2 Copula-Driven Spatial Bayesian Neural Network For Modeling Non-Gaussian Dependence: A Simulation Study |
| topic | Methodology Machine Learning 62H12, 62P10, 65C20, 62F15, 68T07 |
| url | https://arxiv.org/abs/2505.24006 |