F. Bachoc, Calibration And Improved Prediction Of Computer Models By Universal Kriging, Nuclear Science and Engineering, vol.176, issue.1, pp.81-97, 2014.
DOI : 10.13182/NSE12-55

URL : https://hal.archives-ouvertes.fr/hal-01020594

F. Bachoc, Parametric estimation of covariance function in Gaussian-process based Kriging models. Application to uncertainty quantification for computer experiments, 2013.
URL : https://hal.archives-ouvertes.fr/tel-00881002

T. H. Christopher and . Baker, The numerical treatment of integral equations, 1977.

J. Bect, D. Ginsbourger, L. Li, V. Picheny, and E. Vazquez, Sequential design of computer experiments for the estimation of a probability of failure, Statistics and Computing, vol.34, issue.4, pp.773-793, 2012.
DOI : 10.2307/1269548

URL : https://hal.archives-ouvertes.fr/hal-00689580

J. O. Berger, J. M. Bernardo, and D. Sun, The formal definition of reference priors. The Annals of Statistics, pp.905-938, 2009.

J. O. Berger, V. D. Oliveira, and B. Sansó, Objective Bayesian Analysis of Spatially Correlated Data, Journal of the American Statistical Association, vol.96, issue.456, pp.1361-1374, 2001.
DOI : 10.1198/016214501753382282

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

G. Blatman and B. Sudret, Adaptive sparse polynomial chaos expansion based on least angle regression, Journal of Computational Physics, vol.230, issue.6, pp.2345-2367, 2011.
DOI : 10.1016/j.jcp.2010.12.021

B. Echard, N. Gayton, and M. Lemaire, AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation, Structural Safety, vol.33, issue.2, pp.145-154, 2011.
DOI : 10.1016/j.strusafe.2011.01.002

D. Ginsbourger, R. L. Riche, and L. Carraro, Computational Intelligence in Expensive Optimization Problems, volume 2 of Adaptation Learning and Optimization , chapter Kriging Is Well-Suited to Parallelize Optimization, pp.131-162, 2010.

C. Marc, A. Kennedy, and . Hagan, Predicting the output from a complex computer code when fast approximations are avalaible, Biometrika, vol.87, pp.1-13, 2000.

C. Marc, A. Kennedy, and . Hagan, Bayesian Calibration of Computer Models, J. R. Stat. Soc. . Ser. B (Statistical Methodol, vol.63, issue.3, pp.425-464, 2001.

R. Paulo, Default priors for Gaussian processes, The Annals of Statistics, vol.33, issue.2, pp.556-582, 2005.
DOI : 10.1214/009053604000001264

URL : http://arxiv.org/abs/math/0505603

G. Perrin, C. Soize, D. Duhamel, and C. Funfschilling, A Posteriori Error and Optimal Reduced Basis for Stochastic Processes Defined by a Finite Set of Realizations, SIAM/ASA Journal on Uncertainty Quantification, vol.2, issue.1, pp.745-762, 2014.
DOI : 10.1137/130905095

URL : https://hal.archives-ouvertes.fr/hal-01097139

C. Edward-rasmussen and C. K. Williams, Gaussian Processes for Machine Learning, 2006.

C. Robert, The Bayesian Choice, 2007.
DOI : 10.1007/978-1-4757-4314-2

J. Sacks, W. Welch, T. J. Mitchell, and H. P. Wynn, Design and Analysis of Computer Experiments, Statistical Science, vol.4, issue.4, pp.409-435, 1989.
DOI : 10.1214/ss/1177012413

T. J. Santner, B. J. Williams, and W. Notz, The design and analysis of computer experiments. Springer series in statistics, 2003.