189 citations · 763 across the 33 of their papers we have counts for
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A class of multi-resolution approximations for large spatial datasets
Matthias Katzfuss, Wenlong Gong
Gaussian processes are popular and flexible models for spatial, temporal, and functional data, but they are computationally infeasible for large datasets. We discuss Gaussian-proce…
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…
A general framework for Vecchia approximations of Gaussian processes
Matthias Katzfuss, Joseph Guinness
Gaussian processes (GPs) are commonly used as models for functions, time series, and spatial fields, but they are computationally infeasible for large datasets. Focusing on the typ…
Ensemble Kalman methods for high-dimensional hierarchical dynamic space-time models
Matthias Katzfuss, Jonathan R. Stroud, Christopher K. Wikle
We propose a new class of filtering and smoothing methods for inference in high-dimensional, nonlinear, non-Gaussian, spatio-temporal state-space models. The main idea is to combin…