Pushing the Limit: A Hybrid Parallel Implementation of the Multi-resolution Approximation for Massive Data

The multi-resolution approximation (MRA) of Gaussian processes was recently proposed to conduct likelihood-based inference for massive spatial data sets. An advantage of the methodology is that it can be parallelized. We implemented the MRA in C++ for both serial and parallel versions. In the parall...

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Veröffentlicht in:	arXiv.org 2019-05
Hauptverfasser:	Huang, Huang, Blake, Lewis R, Hammerling, Dorit M
Format:	Artikel
Sprache:	eng
Schlagworte:	Approximation C plus plus C++ (programming language) Datasets Distributed memory Gaussian process Inference Mathematical analysis Message passing Parallel processing Parallel programming Spatial data Statistics - Computation
Online-Zugang:	Volltext
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