Parallel inference for massive distributed spatial data using low-rank
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Parallel inference for massive distributed spatial data using low-rank models

6 February 2014

Matthias Katzfuss

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Papers citing "Parallel inference for massive distributed spatial data using low-rank models"

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Title
Scalable Spatio-Temporal Smoothing via Hierarchical Sparse Cholesky Decomposition M. Jurek Matthias Katzfuss 53 9 0 19 Jul 2022
Kryging: Geostatistical analysis of large-scale datasets using Krylov subspace methods Suman Majumder Yawen Guan Brian J. Reich A. Saibaba 42 4 0 24 Dec 2020
Hierarchical sparse Cholesky decomposition with applications to high-dimensional spatio-temporal filtering M. Jurek Matthias Katzfuss 58 20 0 30 Jun 2020
Large-scale Environmental Data Science with ExaGeoStatR Sameh Abdulah Yuxiao Li JIAN-PENG Cao Hatem Ltaief David E. Keyes M. Genton Ying Sun 58 10 0 23 Jul 2019
Multi-Scale Process Modelling and Distributed Computation for Spatial Data A. Zammit‐Mangion J. Rougier 259 11 0 17 Jul 2019
A class of multi-resolution approximations for large spatial datasets Matthias Katzfuss Wenlong Gong GP 168 30 0 24 Oct 2017
FRK: An R Package for Spatial and Spatio-Temporal Prediction with Large Datasets A. Zammit‐Mangion Noel Cressie 87 77 0 23 May 2017
A multi-resolution approximation for massive spatial datasets Matthias Katzfuss 119 245 0 16 Jul 2015