Current models for spatial extremes are concerned with the joint upper (...
A common approach to approximating Gaussian log-likelihoods at scale exp...
Nonstationary Gaussian process models can capture complex spatially vary...
Max-stable processes are the most popular models for high-impact spatial...
The Matérn covariance function is ubiquitous in the application of Gauss...
In traditional extreme value analysis, the bulk of the data is ignored, ...
We propose to use deep learning to estimate parameters in statistical mo...
Many physical datasets are generated by collections of instruments that ...
We present a kernel-independent method that applies hierarchical matrice...
Gaussian random fields (GRF) are a fundamental stochastic model for
spat...
Argo floats measure sea water temperature and salinity in the upper 2,00...