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On the Inference of Applying Gaussian Process Modeling to a
  Deterministic Function
v1v2v3 (latest)

On the Inference of Applying Gaussian Process Modeling to a Deterministic Function

4 February 2020
Wei Cao
ArXiv (abs)PDFHTML

Papers citing "On the Inference of Applying Gaussian Process Modeling to a Deterministic Function"

4 / 4 papers shown
Title
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data
M. Risser
M. Noack
Hengrui Luo
Ronald Pandolfi
GP
99
0
0
07 Nov 2024
Error analysis for a statistical finite element method
Error analysis for a statistical finite element method
Toni Karvonen
F. Cirak
Mark Girolami
23
4
0
19 Jan 2022
Convergence of Gaussian process regression: Optimality, robustness, and
  relationship with kernel ridge regression
Convergence of Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression
Wei Cao
Bing-Yi Jing
55
6
0
20 Apr 2021
Maximum likelihood estimation and uncertainty quantification for
  Gaussian process approximation of deterministic functions
Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions
Toni Karvonen
George Wynne
Filip Tronarp
Chris J. Oates
Simo Särkkä
113
39
0
29 Jan 2020
1