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A Scalable Partitioned Approach to Model Massive Nonstationary
  Non-Gaussian Spatial Datasets

A Scalable Partitioned Approach to Model Massive Nonstationary Non-Gaussian Spatial Datasets

26 November 2020
B. Lee
Jaewoo Park
ArXiv (abs)PDFHTML

Papers citing "A Scalable Partitioned Approach to Model Massive Nonstationary Non-Gaussian Spatial Datasets"

2 / 2 papers shown
Title
When the whole is greater than the sum of its parts: Scaling black-box inference to large data settings through divide-and-conquer
When the whole is greater than the sum of its parts: Scaling black-box inference to large data settings through divide-and-conquer
Emily C. Hector
Amanda Lenzi
434
1
0
31 Dec 2024
Modelling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal
  Data using FRK
Modelling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data using FRK
Matthew Sainsbury-Dale
A. Zammit‐Mangion
Noel Cressie
50
6
0
06 Oct 2021
1