2 citations · 3 across the 6 of their papers we have counts for
5 papers · 1 filter
Scalable generative modeling of non-Gaussian spatio-temporal fields via autoregressive Gaussian processes
Carrie J. Lei-Cramer, Jian Cao, Matthias Katzfuss
Generative modeling of spatio-temporal fields is crucial for a variety of applications, including stochastic weather generators and climate-model surrogates. However, many such fie…
Bayesian nonstationary and nonparametric covariance estimation for large spatial data
Brian Kidd, Matthias Katzfuss
In spatial statistics, it is often assumed that the spatial field of interest is stationary and its covariance has a simple parametric form, but these assumptions are not appropria…
Vecchia-Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data
Daniel Zilber, Matthias Katzfuss
Generalized Gaussian processes (GGPs) are highly flexible models that combine latent GPs with potentially non-Gaussian likelihoods from the exponential family. GGPs can be used in…
Multi-resolution filters for massive spatio-temporal data
Marcin Jurek, Matthias Katzfuss
Spatio-temporal data sets are rapidly growing in size. For example, environmental variables are measured with ever-higher resolution by increasing numbers of automated sensors moun…
Vecchia approximations of Gaussian-process predictions
Matthias Katzfuss, Joseph Guinness, Wenlong Gong +1
Gaussian processes (GPs) are highly flexible function estimators used for geospatial analysis, nonparametric regression, and machine learning, but they are computationally infeasib…