R. J. Adler and J. E. Taylor, Random Fields and Geometry, 2010.
DOI : 10.1137/1.9780898718980

R. [. Bellman and . Calif, DYNAMIC PROGRAMMING AND LAGRANGE MULTIPLIERS, Defense Technical Information Center, 1956.
DOI : 10.1073/pnas.42.10.767
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC528332

]. N. Cre93 and . Cressie, Statistics for spatial data Wiley series in probability and mathematical statistics: Applied probability and statistics, 1993.

D. [. Durrande, O. Ginsbourger, and . Roustant, Additive Covariance kernels for high-dimensional Gaussian Process modeling, Annales de la facult?? des sciences de Toulouse Math??matiques, vol.21, issue.3, pp.481-499, 2012.
DOI : 10.5802/afst.1342
URL : https://hal.archives-ouvertes.fr/hal-00644934

D. [. Durrande, O. Ginsbourger, L. Roustant, and . Carraro, ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis, Journal of Multivariate Analysis, vol.115, issue.0, pp.57-67, 2013.
DOI : 10.1016/j.jmva.2012.08.016
URL : https://hal.archives-ouvertes.fr/hal-00601472

J. [. Durrande, M. Hensman, N. D. Rattray, and . Lawrence, Gaussian process models for periodicity detection, p.805468, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00805468

G. [. Dietrich and . Newsam, Fast and Exact Simulation of Stationary Gaussian Processes through Circulant Embedding of the Covariance Matrix, SIAM Journal on Scientific Computing, vol.18, issue.4, pp.1088-1107, 1997.
DOI : 10.1137/S1064827592240555

]. J. Doo53 and . Doob, Stochastic processes. Wiley publications in statistics, 1953.

X. [. Gaetan and . Guyon, Spatial Statistics and Modeling. Springer Series in Statistics, 2009.

D. Ginsbourger and D. Schuhmacher, Spatial statistics. Lecture notes from the Spatial Statistics course in fall, 2011.

]. J. Hfa-+-13, N. Hensman, R. Fusi, N. Andrade, A. Durrande et al., Gpy (version 0.3.2) -gaussian processes framework in python, 2013.

T. Hastie and R. Tibshirani, Generalized Additive Models. Monographs on statistics and applied probability, 1990.

J. D. Hunter, Matplotlib: A 2D Graphics Environment, Computing in Science & Engineering, vol.9, issue.3, pp.90-95, 2007.
DOI : 10.1109/MCSE.2007.55

R. [. Mckay, W. J. Beckman, and . Conover, A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, vol.21, issue.2, pp.239-245, 1979.

]. T. Oli07 and . Oliphant, Python for scientific computing, Computing in Science & Engineering, vol.9, pp.10-20, 2007.

[. Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2008.

M. Scheuerer, A Comparison of Models and Methods for Spatial Interpolation in Statistics and Numerical Analysis, 2009.

A. [. Schölkopf and . Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Adaptive computation and machine learning, 2002.

M. L. Stein, Interpolation of Spatial Data: Some Theory for Kriging Springer Series in Statistics, Tre13] C. Tretter. Functional analysis. Lecture notes from the Functional Analysis course in spring 2013 at the University of Bern, 1999.
DOI : 10.1007/978-1-4612-1494-6

V. Tarieladze and N. Vakhania, Disintegration of Gaussian measures and average-case optimal algorithms, Journal of Complexity, vol.23, issue.4-6, pp.851-866, 2007.
DOI : 10.1016/j.jco.2007.04.005