Tucker Tensor Analysis of Matérn Functions in Spatial Statistics

In this work, we describe advanced numerical tools for working with multivariate functions and for the analysis of large data sets. These tools will drastically reduce the required computing time and the storage cost, and, therefore, will allow us to consider much larger data sets or finer meshes. C...

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Vydané v:	Journal of computational methods in applied mathematics Ročník 19; číslo 1; s. 101 - 122
Hlavní autori:	Litvinenko, Alexander, Keyes, David, Khoromskaia, Venera, Khoromskij, Boris N., Matthies, Hermann G.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Minsk De Gruyter 01.01.2019 Walter de Gruyter GmbH
Predmet:	60H15 60H35 65N25 Bayesian Update Computing costs Computing time Convergence Covariance matrix Datasets Decomposition Economic models Format Fourier Transform Geostatistical Optimal Design Hilbert Tensor Kalman Filter Kriging Loglikelihood Surrogate Low-Rank Tensor Approximation Matérn Covariance Parameters Quadratic forms Tensor analysis Tensors
ISSN:	1609-4840, 1609-9389
On-line prístup:	Získať plný text
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