132 citations · 165 across the 7 of their papers we have counts for
7 papers
Fast covariance parameter estimation of spatial Gaussian process models using neural networks
Florian Gerber, Douglas W. Nychka
Gaussian processes (GPs) are a popular model for spatially referenced data and allow descriptive statements, predictions at new locations, and simulation of new fields. Often a few…
Identification of Dominant Features in Spatial Data
Roman Flury, Florian Gerber, Bernhard Schmid +1
Dominant features of spatial data are connected structures or patterns that emerge from location-based variation and manifest at specific scales or resolutions. To identify dominan…
Parallel cross-validation: a scalable fitting method for Gaussian process models
Florian Gerber, Douglas W. Nychka
Gaussian process (GP) models are widely used to analyze spatially referenced data and to predict values at locations without observations. In contrast to many algorithmic procedure…
optimParallel: an R Package Providing Parallel Versions of the Gradient-Based Optimization Methods of optim()
Florian Gerber, Reinhard Furrer
The R package optimParallel provides a parallel version of the gradient-based optimization methods of optim(). The main function of the package is optimParallel(), which has the sa…
A Case Study Competition Among Methods for Analyzing Large Spatial Data
Matthew J. Heaton, Abhirup Datta, Andrew Finley +10
The Gaussian process is an indispensable tool for spatial data analysts. The onset of the "big data" era, however, has lead to the traditional Gaussian process being computationall…
dotCall64: An Efficient Interface to Compiled C/C++ and Fortran Code Supporting Long Vectors
Florian Gerber, Kaspar Mösinger, Reinhard Furrer
The R functions .C() and .Fortran() can be used to call compiled C/C++ and Fortran code from R. This so-called foreign function interface is convenient, since it does not require a…