Bayesian Projected Calibration of Computer Models

DOI10.1080/01621459.2020.1753519zbMath1506.62271arXiv1803.01231OpenAlexW3015634775MaRDI QIDQ5881976

Fangzheng Xie, Yanxun Xu

Publication date: 14 March 2023

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1803.01231

zbMATH Keywords

asymptotic normalitysemiparametric efficiencyuncertainty quantificationcomputer experiment\(L_2\)-projection

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Gaussian processes (60G15) Bayesian inference (62F15) Bayesian problems; characterization of Bayes procedures (62C10)

Related Items (8)

Penalized Projected Kernel Calibration for Computer Models ⋮ Fast Calibration for Computer Models with Massive Physical Observations ⋮ Calibration of Inexact Computer Models with Heteroscedastic Errors ⋮ A Reproducing Kernel Hilbert Space Approach to Functional Calibration of Computer Models ⋮ Deep Gaussian processes for calibration of computer models (with discussion) ⋮ A theoretical framework of the scaled Gaussian stochastic process in prediction and calibration ⋮ A fast and calibrated computer model emulator: an empirical Bayes approach ⋮ Variational inference with vine copulas: an efficient approach for Bayesian computer model calibration

Uses Software

• gss
• AdaGrad

Cites Work

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