smashGP: Large-scale Spatial Modeling via Matrix-free Gaussian Processes

IF 1.4 2区数学 Q2 STATISTICS & PROBABILITY

Journal of Computational and Graphical Statistics Pub Date : 2024-05-22 DOI:10.1080/10618600.2024.2353653

Lucas Erlandson, Ana María Estrada Gómez, Edmond Chow, Kamran Paynabar

引用次数: 0

Abstract

Gaussian processes are essential for spatial data analysis. Not only do they allow the prediction of unknown values, but they also allow for uncertainty quantification. However, in the era of big d...

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smashGP：通过无矩阵高斯过程进行大规模空间建模

高斯过程对空间数据分析至关重要。它们不仅可以预测未知值，还可以量化不确定性。然而，在大数据时代，高斯过程的应用却面临着巨大的挑战。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computational and Graphical Statistics 数学-统计学与概率论

CiteScore

3.50

自引率

8.30%

发文量

153

审稿时长

>12 weeks

期刊介绍： The Journal of Computational and Graphical Statistics (JCGS) presents the very latest techniques on improving and extending the use of computational and graphical methods in statistics and data analysis. Established in 1992, this journal contains cutting-edge research, data, surveys, and more on numerical graphical displays and methods, and perception. Articles are written for readers who have a strong background in statistics but are not necessarily experts in computing. Published in March, June, September, and December.