A theoretical framework of the scaled Gaussian stochastic process in prediction and calibration

DOI10.48550/arXiv.1807.03829zbMath1498.62016arXiv1807.03829MaRDI QIDQ96361

Long Wang, Mengyang Gu, Fangzheng Xie

Publication date: 10 July 2018

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1807.03829

zbMATH Keywords

convergenceinterpretabilitymodel misspecificationBayesian priororthogonal series representationscaled Gaussian stochastic process prior

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Bayesian inference (62F15) Applications of statistics in engineering and industry; control charts (62P30) Foundations and philosophical topics in statistics (62A01)

Related Items (3)

Penalized Projected Kernel Calibration for Computer Models ⋮ Probabilistic forecast of nonlinear dynamical systems with uncertainty quantification ⋮ RobustCalibration

Uses Software

• L-BFGS
• deSolve
• diffEq

Cites Work

• Unnamed Item
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