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Found 4 papers, 2 papers with code
Kryging: Geostatistical analysis of large-scale datasets using Krylov subspace methods
Analyzing massive spatial datasets using Gaussian process model poses computational challenges.
Methodology Computation
A spectral adjustment for spatial confounding
We show that this assumption in the spectral domain is equivalent to adjusting for global-scale confounding in the spatial domain by adding a spatially smoothed version of the treatment variable to the mean of the response variable.
Methodology
Probabilistic Projections of Baseline 21st Century CO$_2$ Emissions Using a Simple Calibrated Integrated Assessment Model
Our global sensitivity analysis identifies several key economic drivers of uncertainty in future emissions and shows important higher-level interactions between economic and technological parameters, while population uncertainties are less important.
Applications
Nearest-Neighbor Neural Networks for Geostatistics
We propose a more flexible spatial prediction method based on the Nearest-Neighbor Neural Network (4N) process that embeds deep learning into a geostatistical model.
