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title={A frequentist approach to computer model calibration},
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title={Combining expert opinions in prior elicitation},
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@Manual{CaliCo,
title = {CaliCo: Code Calibration in a Bayesian Framework},
author = {Mathieu Carmassi},
year = {2018},
note = {R package version 0.1.0},
url = {https://CRAN.R-project.org/package=CaliCo}
}
@article{plumlee2017,
author = {Matthew Plumlee},
title = {Bayesian Calibration of Inexact Computer Models},
journal = {Journal of the American Statistical Association},
volume = {112},
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year = {2017},
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doi = {10.1080/01621459.2016.1211016},
URL = {https://doi.org/10.1080/01621459.2016.1211016},
eprint = {https://doi.org/10.1080/01621459.2016.1211016}
}
@article{gu2017,
author = {Mengyang Gu and Long Wang},
title = {Scaled Gaussian Stochastic Process for Computer Model Calibration and Prediction},
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title={Efficient calibration for imperfect computer models},
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@article{tuo2016theoretical,
title={A theoretical framework for calibration in computer models: parametrization, estimation and convergence properties},
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pages={767--795},
year={2016},
publisher={SIAM}
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title={Quantifying uncertainty in an industrial approach: an emerging consensus in an old epistemological debate},
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title={Efficient global optimization of expensive black-box functions},
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@inproceedings{da2012gaussian,
title={Gaussian process modeling with inequality constraints},
author={Da Veiga, S{\'e}bastien and Marrel, Amandine},
booktitle={Annales de la Facult{\'e} des Sciences de Toulouse},
volume={21},
number={3},
pages={529--555},
year={2012}
}