Assessment of uncertainty in computer experiments from Universal to Bayesian Kriging
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DOI: 10.1002/asmb.743
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References listed on IDEAS
- Jeremy E. Oakley & Anthony O'Hagan, 2004. "Probabilistic sensitivity analysis of complex models: a Bayesian approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 751-769, August.
- Dale Zimmerman & Noel Cressie, 1992. "Mean squared prediction error in the spatial linear model with estimated covariance parameters," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 44(1), pages 27-43, March.
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Cited by:
- Risk, J. & Ludkovski, M., 2016. "Statistical emulators for pricing and hedging longevity risk products," Insurance: Mathematics and Economics, Elsevier, vol. 68(C), pages 45-60.
- Loïc Iapteff & Julien Jacques & Matthieu Rolland & Benoit Celse, 2021. "Reducing the number of experiments required for modelling the hydrocracking process with kriging through Bayesian transfer learning," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1344-1364, November.
- James Risk & Michael Ludkovski, 2015. "Statistical Emulators for Pricing and Hedging Longevity Risk Products," Papers 1508.00310, arXiv.org, revised Sep 2015.
- Roustant, Olivier & Ginsbourger, David & Deville, Yves, 2012. "DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i01).
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