A Theoretical Framework of the Scaled Gaussian Stochastic Process in Prediction and Calibration-Reference-Cited by-同舟云学术

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A Theoretical Framework of the Scaled Gaussian Stochastic Process in Prediction and Calibration

Published:2022-10-31 Issue:4 Volume:10 Page:1435-1460
ISSN:2166-2525
Container-title:SIAM/ASA Journal on Uncertainty Quantification
language:en
Short-container-title:SIAM/ASA J. Uncertainty Quantification

Author:

Gu Mengyang¹,Xie Fangzheng²^ORCID,Wang Long³

Affiliation:

1. Department of Statistics and Applied Probability, University of California, Santa Barbara, Santa Barbara, CA 93106-3110 USA ().

2. Department of Statistics, Indiana University, Bloomington, Bloomington, IN 47408 USA ().

3. Department of Applied Mathematical and Statistics, Johns Hopkins University, Baltimore, MD 21218 USA ().

Funder

Division of Mathematical Sciences

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Applied Mathematics,Discrete Mathematics and Combinatorics,Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Reference30 articles.

1. Magma reservoir failure and the onset of caldera collapse at Kīlauea Volcano in 2018

2. Abundant carbon in the mantle beneath Hawai‘i

3. Quantification of Model Uncertainty: Calibration, Model Discrepancy, and Identifiability

4. A Framework for Validation of Computer Models

5. Anisotropic function estimation using multi-bandwidth Gaussian processes

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1. Efficient calibration of computer models with multivariate output;Journal of Multivariate Analysis;2024-07

2. Probabilistic forecast of nonlinear dynamical systems with uncertainty quantification;Physica D: Nonlinear Phenomena;2024-01

3. A review on computer model calibration;WIREs Computational Statistics;2024-01

4. Calibration of Imperfect Geophysical Models by Multiple Satellite Interferograms with Measurement Bias;Technometrics;2023-04-05

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