Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration | Paper Citation Record

Observation 0c295dc8-0ea9-480e-8339-050300846778 · outbound

This paper cites Learning about physical param- eters: The importance of model discrepancy.Inverse Problems, 30(11):114007, 2014.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Learning about physical param- eters: The importance of model discrepancy.Inverse Problems, 30(11):114007, 2014

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This paper cites Bayesian sensitivity analysis of a cardiac cell model using a Gaussian process emulator.PloS one, 10(6):e0130252, 2015.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Bayesian sensitivity analysis of a cardiac cell model using a Gaussian process emulator.PloS one, 10(6):e0130252, 2015

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This paper cites Bayesian variable selection with related predictors.Canadian Journal of Statistics, 24(1):17–36, 1996.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Bayesian variable selection with related predictors.Canadian Journal of Statistics, 24(1):17–36, 1996

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This paper cites Gaussian process emulation of spatio-temporal outputs of a 2D inland flood model.Water Research, 225:119100, 2022.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Gaussian process emulation of spatio-temporal outputs of a 2D inland flood model.Water Research, 225:119100, 2022

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This paper cites Simulation of geological domains using the plurigaussian model: New developments and computer programs.Computers & Geosciences, 33(9):1189–1201, 2007.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Simulation of geological domains using the plurigaussian model: New developments and computer programs.Computers & Geosciences, 33(9):1189–1201, 2007

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Observation 75d13a41-2e52-4f65-87a7-c5bcc6ad3d6b · outbound

This paper cites Combining experimental data and computer sim- ulations, with an application to flyer plate experiments.Bayesian Analysis, 1(4):765–792, 2006.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Combining experimental data and computer sim- ulations, with an application to flyer plate experiments.Bayesian Analysis, 1(4):765–792, 2006

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Observation 525fa044-0226-4acd-b085-220d31942175 · outbound

This paper cites Illustration of Bayesian inference in normal data models using Gibbs sampling.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Illustration of Bayesian inference in normal data models using Gibbs sampling

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This paper cites Scaled Gaussian stochastic process for com- puter model calibration and prediction.SIAM/ASA Journal on Uncertainty Quantification, 6(4):1555–1583, 2018.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Scaled Gaussian stochastic process for com- puter model calibration and prediction.SIAM/ASA Journal on Uncertainty Quantification, 6(4):1555–1583, 2018

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This paper cites Computer model calibration using high-dimensional output.Journal of the American Sta- tistical Association, 103(482):570–583, 2008.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Computer model calibration using high-dimensional output.Journal of the American Sta- tistical Association, 103(482):570–583, 2008

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Observation 4874fd4c-389a-4d81-a7c9-dc4b16d97ddc · outbound

This paper cites Computer model calibration using the ensemble Kalman filter.Technometrics, 55(4):488– 500, 2013.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Computer model calibration using the ensemble Kalman filter.Technometrics, 55(4):488– 500, 2013

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This paper cites A Bayesian approach for parameter estimation and prediction using a computationally intensive model.Journal of Physics G: Nuclear and Particle Physics, 42(3):034009, 2015.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration A Bayesian approach for parameter estimation and prediction using a computationally intensive model.Journal of Physics G: Nuclear and Particle Physics, 42(3):034009, 2015

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Unresolved cited work

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Observation aa8ee7a3-3e30-4709-8154-d465128e781a · outbound

This paper cites Space-filling designs for computer experiments: A review.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Space-filling designs for computer experiments: A review

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Observation 78bf7f95-9898-41a7-b6a8-242ebc983f48 · outbound

This paper cites Bayesian calibration of computer mod- els.Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(3):425–464, 2001.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Bayesian calibration of computer mod- els.Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(3):425–464, 2001

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Observation 90afe6d3-9eb3-4314-9d05-ab8d4f186364 · outbound

This paper cites Kortelainen, J.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Kortelainen, J

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Observation 76500c1e-0a12-4d2e-bf50-980381ab064c · outbound

This paper cites Springer, 1991.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Springer, 1991

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Observation f387395d-5f08-4a21-9fac-fee7e426a366 · outbound

This paper cites Probabilistic non-linear principal compo- nent analysis with Gaussian process latent variable models.Journal of Machine Learning Research, 6(11), 2005.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Probabilistic non-linear principal compo- nent analysis with Gaussian process latent variable models.Journal of Machine Learning Research, 6(11), 2005

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This paper cites Fast sparse Gaussian process methods: The informative vector machine.Advances in Neural Information Processing Systems, 15, 2002.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Fast sparse Gaussian process methods: The informative vector machine.Advances in Neural Information Processing Systems, 15, 2002

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Unresolved cited work

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This paper cites NPTool: A simulation and analysis framework for low-energy nuclear physics experiments.Journal of Physics G: Nuclear and Particle Physics, 43(4):045113, 2016.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration NPTool: A simulation and analysis framework for low-energy nuclear physics experiments.Journal of Physics G: Nuclear and Particle Physics, 43(4):045113, 2016

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This paper cites Multi-kernel Gaussian processes.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Multi-kernel Gaussian processes

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This paper cites Computer emulation with nonstation- ary Gaussian processes.SIAM/ASA Journal on Uncertainty Quantification, 4(1):26–47, 2016.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Computer emulation with nonstation- ary Gaussian processes.SIAM/ASA Journal on Uncertainty Quantification, 4(1):26–47, 2016

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Unresolved cited work

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This paper cites Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification

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This paper cites Neutron drip line in the CA region from Bayesian model averaging.Phys.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Neutron drip line in the CA region from Bayesian model averaging.Phys

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This paper cites Bayesian approach to model-based extrapolation of nuclear observables.Phys.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Bayesian approach to model-based extrapolation of nuclear observables.Phys

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Unresolved cited work

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This paper cites Compu- tational fluid dynamics simulation of airflow and aerosol deposition in human lungs.Annals of Biomedical Engineering, 31:374–390, 2003.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Compu- tational fluid dynamics simulation of airflow and aerosol deposition in human lungs.Annals of Biomedical Engineering, 31:374–390, 2003

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Nonstationary covariance functions for Gaussian process regression.Advances in Neural Information Processing Systems, 16, 2003

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Controlling extrapolations of nu- clear properties with feature selection.Physics Letters B, 833:137336, 2022

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Nonstation- ary Gaussian process regression using point estimates of local smoothness

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Bayesian calibration of inexact computer models.Journal of the American Statistical Association, 112(519):1274–1285, 2017

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Bayesian additive regression tree calibration of complex high-dimensional computer models.Technometrics, 58(2):166–179, 2016

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Designs for computer experiments.Technometrics, 31(1):41–47, 1989

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Computer simu- lation of inclusive pion nuclear reactions.Nuclear Physics A, 484(3-4):557–592, 1988

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration The Design and Analysis of Computer Experiments, volume 1

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Calibration of energy density functionals with deformed nuclei.Journal of Physics G: Nuclear and Particle Physics, 47(7):074001, 2020

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This paper cites IoP Publishing, 2019.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration IoP Publishing, 2019

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Variable noise and dimensionality reduction for sparse Gaussian processes

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Local and global sparse Gaussian process approximations

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This paper cites A review on computer model calibration.Wiley Interdisciplinary Reviews: Computational Statistics, 16(1):e1645, 2024.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration A review on computer model calibration.Wiley Interdisciplinary Reviews: Computational Statistics, 16(1):e1645, 2024

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Bayesian Gaussian process latent variable model

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Efficient calibration for imperfect computer models

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Utama and J

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Utama and J

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Utama, J

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Springer Science & Business Media, 2008

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Gaussian processes for regression

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration MIT press Cambridge, MA, 2006

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Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration John Wiley & Sons, 2011

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This paper cites Application of computer simulation method in deep underground engineering of complicated geological condition.IEEE Access, 8:174943–174963, 2020.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Application of computer simulation method in deep underground engineering of complicated geological condition.IEEE Access, 8:174943–174963, 2020

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This paper cites Probabilis- tic power flow based on a Gaussian process emulator.IEEE Transactions on Power Systems, 35(4):3278–3281, 2020.

Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration Probabilis- tic power flow based on a Gaussian process emulator.IEEE Transactions on Power Systems, 35(4):3278–3281, 2020

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