A2 Copula-Driven Spatial Bayesian Neural Network For Modeling Non-Gaussian Dependence: A Simulation Study

• URL: http://arxiv.org/abs/2505.24006v1
• Date: Thu, 29 May 2025 21:02:44 GMT
• Title: A2 Copula-Driven Spatial Bayesian Neural Network For Modeling Non-Gaussian Dependence: A Simulation Study
• Authors: Agnideep Aich, Sameera Hewage, Md Monzur Murshed, Ashit Baran Aich, Amanda Mayeaux, Asim K. Dey, Kumer P. Das, Bruce Wade,
• Abstract summary: A2-SBNN is a predictive spatial model designed to map coordinates to continuous fields.<n>A2-SBNN consistently delivers high accuracy across a wide range of dependency strengths.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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