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stat.ME2026
MCMC-Free Uncertainty Quantification for Deep Generative Inference for Spatially Varying Coefficient Models at Scale
Yeseul Jeon, Aaron Scheffler, Rajarshi Guhaniyogi
Varying coefficient (VC) regression models have become indispensable tools in spatial data analysis, providing unmatched flexibility in capturing complex, nonlinear relationships a…
stat.ME2026
Deep Probabilistic Spatial Modeling for Multivariate Mixed-Type Responses
Yeseul Jeon, Kyeong Eun Lee, Joon Jin Song
Many scientific applications involve mixed spatially indexed outcomes of heterogeneous types that are driven by shared latent mechanisms. Modeling such data is challenging due to c…
stat.ME2026
Uncertainty-Aware Neural Multivariate Geostatistics
Yeseul Jeon, Aaron Scheffler, Rajarshi Guhaniyogi
We propose Deep Neural Coregionalization, a scalable framework for uncertainty-aware multivariate geostatistics. DNC models multivariate spatial effects through spatially varying l…