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stat.ME2026
Bayesian Spatiotemporal Wombling
Aritra Halder, Didong Li, Sudipto Banerjee
Stochastic process models for spatiotemporal data underlying random fields find substantial utility in a range of scientific disciplines. Subsequent to predictive inference on the…
stat.ME2025
The Nearest-Neighbor Derivative Process: Modeling Spatial Rates of Change in Massive Datasets
Jiawen Chen, Aritra Halder, Yun Li +2
Gaussian processes (GPs) are instrumental in modeling spatial processes, offering precise interpolation and prediction capabilities across fields such as environmental science and…