A Scalable Method to Exploit Screening in Gaussian Process Models with Noise-Reference-Cited by-同舟云学术

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A Scalable Method to Exploit Screening in Gaussian Process Models with Noise

Published:2023-06-30 Issue: Volume: Page:1-11
ISSN:1061-8600
Container-title:Journal of Computational and Graphical Statistics
language:en
Short-container-title:Journal of Computational and Graphical Statistics

Author:

Geoga Christopher J.¹^ORCID,Stein Michael L.¹

Affiliation:

1. Department of Statistics, Rutgers University, New Brunswick, NJ

Funder

US Department of Energy, Office of Science

Office of Advanced Scientific Computing Research

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Discrete Mathematics and Combinatorics,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/10618600.2023.2218027

Reference52 articles.

1. Fast Direct Methods for Gaussian Processes

2. A Matrix-free Approach for Solving the Parametric Gaussian Process Maximum Likelihood Problem

3. Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix

4. Beckman, P. G., Geoga, C. J., Stein, M. L., and Anitescu, M. (2022), “Scalable Computations for Nonstationary Gaussian Processes,” Technical Report arXiv:2206.05220, arXiv. arXiv:2206.05220 [stat] type: article.

5. Linear-Cost Covariance Functions for Gaussian Random Fields

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