Open Access
Volume 23, 2019
Page(s) 245 - 270
Published online 03 May 2019
  1. F. Bachoc, Cross validation and maximum likelihood estimation of hyper-parameters of Gaussian processes with model misspecification. Comput. Stat. Data Anal. 66 (2013) 55–69. [Google Scholar]
  2. F. Bachoc, Parametric estimation of covariance function in Gaussian-process based Kriging models. Application to uncertainty 520 quantification for computer experiments. Ph.D. thesis. Université Paris-Diderot – Paris VII (2013). [Google Scholar]
  3. C.T.H. Baker, The Numerical Treatment of Integral Equations. Clarendon Press, Oxford (1977). [Google Scholar]
  4. J. Bect, D. Ginsbourger, L. Li, V. Picheny and E. Vazquez, Sequential design of computer experiments for the estimation of a probability of failure. Stat. Comput. 22 (2012) 773–793. [Google Scholar]
  5. J.O. Berger, V. De Oliveira and B. Sansó, Objective Bayesian analysis of spatially correlated data. J. Am. Stat. Assoc. 96 (2001) 1361–1374. [Google Scholar]
  6. B.J. Bichon, M.S. Eldred, L.P. Swiler, S. Mahadevan and J.M. Mcfarland, Efficient global reliability analysis for nonlinear implicit performance functions. AIAA J. 46 (2008) 2459–2468. [Google Scholar]
  7. C. Chevalier, J. Bect, D. Ginsbourger and E. Vazquez, Fast parallel Kriging-based stepwise uncertainty reduction with application to the identification of an excursion set. Technometrics 56 (2014) 455–465. [Google Scholar]
  8. A. Damianou and N.D. Lawrence, Deep Gaussian processes, in Proceedings of the Sixteenth International Workshop on Artificial Intelligence and Statistics (AISTATS), AISTATS ’13, pages 207–215, JMLR W&CP 31, edited by C. Carvalho and P. Ravikumar (2013). [Google Scholar]
  9. B. Echard, N. Gayton and M. Lemaire, AK-MCS: an active learning reliability method combining Kriging and Monte Carlo simulation. Struct. Saf . 33 (2011) 145–154. [CrossRef] [Google Scholar]
  10. K.T. Fang and D.K. Lin, Uniform experimental designs and their applications in industry. Handb. Stat. 22 (2003) 131–178. [Google Scholar]
  11. K.T. Fang, R. Li and A. Sudjianto, Design and Modeling for Computer Experiments. Computer Science and Data Analysis Series. Chapman Hall, London (2006). [Google Scholar]
  12. D. Ginsbourger, R. Le Riche and L. Carraro, Kriging is well-suited to parallelize optimization, in Computational Intelligence in Expensive Optimization Problems. Vol. 2 of Adaptation Learning and Optimization. Springer, Berlin, Heidelberg (2010) 131–162. [CrossRef] [Google Scholar]
  13. R.B. Gramacy and H.K.H. Lee, Cases for the nugget in modeling computer experiments. Stat. Comput. 22 (2012) 713–722. [Google Scholar]
  14. R. Gramacy and H. Lian, Gaussian process single-index models as emulators for computer experiments. Technometrics 54 (2012) 30–41. [Google Scholar]
  15. C. Helbert, D. Dupuy and L. Carraro, Assessment of uncertainty in computer experiments, from Universal to Bayesian Kriging. Appl. Stoch. Model. Bus. Ind. 25 (2009) 99–113. [CrossRef] [Google Scholar]
  16. R. Hu and M. Ludkovski, Sequential design for ranking response surfaces. SIAM/ASA J. Uncertain. Quantif . 5 (2017) 212–239. [Google Scholar]
  17. M.C. Kennedy and A. O’Hagan, Predicting the output from a complex computer code when fast approximations are avalaible. Biometrika 87 (2000) 1–13. [Google Scholar]
  18. M.C. Kennedy and A. O’Hagan, Bayesian calibration of computer models. J. Royal Stat. Soc. Ser. B (Stat. Methodol.) 63 (2001) 425–464. [Google Scholar]
  19. J.P.C. Kleijnen, Regression and Kriging metamodels with their experimental designs in simulation: a review. Eur. J. Oper. Res. 256 (2017) 1–16. [Google Scholar]
  20. L. Le Gratiet, Bayesian analysis of hierarchical multifidelity codes. SIAM/ASA J. Uncertain. Quantif . 1 (2013) 244–269. [CrossRef] [Google Scholar]
  21. L. Le Gratiet and J. Garnier, Recursive co-Kriging model for design of computer experiments with multiple levels of fidelity. Int. J. Uncertain. Quantif . 4 (2014) 365–386. [Google Scholar]
  22. A. Papoulis and S.U. Pillai, Probability, Random Variables and Stochastic Processes. McGraw-Hill, Boston (2002). [Google Scholar]
  23. R. Paulo, Default priors for Gaussian processes. Ann. Stat. 33 (2005) 556–582. [Google Scholar]
  24. P. Perdikaris, M. Raissi, A. Damianou, N.D. Lawrence and G.E. Karniadakis, Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling. Proc. R. Soc. Lond. A: Math. Phys. Eng. Sci. 473 (2017) 20160751. [Google Scholar]
  25. G. Perrin, Active learning surrogate models for the conception of systems with multiple failure modes. Reliab. Eng. Syst. Saf . 149 (2016) 130–136. [Google Scholar]
  26. G. Perrin and C. Cannamela, A repulsion-based method for the definition and the enrichment of optimized space filling designs in constrained input spaces. J. Soc. Française de Stat. 158 (2017) 37–67. [Google Scholar]
  27. G. Perrin, C. Soize, S. Marque-Pucheu and J. Garnier, Nested polynomial trends for the improvement of Gaussian process-based predictors. J. Comput. Phys. 346 (2017) 389–402. [Google Scholar]
  28. V. Picheny and D. Ginsbourger, A nonstationary space-time Gaussian process model for partially converged simulations. SIAM/ASA J. Uncertain. Quantif . 1 (2013) 37–67. [Google Scholar]
  29. C.E. Rasmussen and C.K.I. Williams, Gaussian Processes for Machine Learning. The MIT Press, Cambridge (2006). [Google Scholar]
  30. C. Robert, The Bayesian Choice. Springer-Verlag, New York (2007). [Google Scholar]
  31. J. Sacks, W. Welch, T.J. Mitchell and H.P. Wynn, Design and analysis of computer experiments. Stat. Sci. 4 (1989) 409–435. [Google Scholar]
  32. T.J. Santner, B.J. Williams and W. Notz, The Design and Analysis of Computer Experiments. Springer Series in Statistics. Springer, New York (2003). [CrossRef] [Google Scholar]
  33. M.L. Stein, Interpolation of Spatial Data: Some Theory for Kriging. Springer, New York (1999). [CrossRef] [Google Scholar]
  34. R. Stroh, S. Demeyer, N. Fischer, J. Bect and E. Vazquez, Sequential design of experiments to estimate a probability of exceedinga threshold in a multi-fidelity stochastic simulator, in 61th World Statistics Congress of the International Statistical Institute (ISI 2017), Marrakech, Morocco, July 2017 (2017). [Google Scholar]
  35. R. Tuo, C.F Jeff Wu and D. Yu, Surrogate modeling of computer experiments with different mesh densities. Technometrics 56 (2014) 372–380. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.