REX-SUB combines a randomized exchange algorithm with Vecchia approximation to choose subsamples that minimize mean squared prediction error and interval scores in large-scale spatial GPs.
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A random scale mixture process with amortized Bayesian inference enables scalable modeling of spatially dependent extreme temperatures and associated heat risks.
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REX-SUB: A Scalable Subsampling Strategy for Modeling Large Spatial Datasets
REX-SUB combines a randomized exchange algorithm with Vecchia approximation to choose subsamples that minimize mean squared prediction error and interval scores in large-scale spatial GPs.
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Spatial Extremes at Scale: A Case Study of Surface Skin Temperature and Heat Risk in the United States
A random scale mixture process with amortized Bayesian inference enables scalable modeling of spatially dependent extreme temperatures and associated heat risks.