Y. Auffray, P. Barbillon, and J. Marin, Maximin design on non hypercube domains and kernel interpolation, Bor76] C. Borell. Gaussian Radon measures on locally convex spaces, pp.703-712265, 1976.
DOI : 10.1007/s11222-011-9273-9

A. Berlinet and C. Thomas-agnan, Reproducing Kernel Hilbert Spaces in Probability and Statistics, 2004.
DOI : 10.1007/978-1-4419-9096-9

F. Cucker and S. Smale, On the mathematical foundations of learning. Bulletin (new series, pp.1-49, 2002.

R. M. Dudley, J. Feldman, and L. L. Cam, On seminorms and probabilities, and abstract Wiener spaces, Annals of Mathematics, pp.390-408, 1971.
DOI : 10.2307/1970780

H. Dette, A. Pepelyshev, A. M. Zhigljavskydud02-]-r, and . Dudley, Optimal design for linear models with correlated observations, For85] R.M. Fortet. Les opérateurs intégraux dont le noyau est une covariance. Trabajos de Estadistica y de Investigacion Operativa, pp.143-176133, 1985.
DOI : 10.1214/12-AOS1079
URL : http://arxiv.org/abs/1303.2863

B. Gauthier, X. [. Bay, L. Gauthier, and . Pronzato, Spectral approach for kernel-based interpolation Annales de la faculté des sciences de Toulouse, 21 (num. 3) Approximation of IMSE optimal designs of experiments via quadrature rule and spectral decomposition, Communications in Statistics ? Simulation and Computation, pp.439-479, 2012.

W. Hackbuschkre99-]-r and . Kress, Integral Equations: Theory and Numerical Treatment Linear Integral Equations Spectral Methods for Uncertainty Quantification, 1995.

H. Niederreiter, SIAM, Philadelphia , 1992. [R C13] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, I. Williams. Gaussian Processes for Machine Learning, 2006.

W. A. Stein, The Sage Development Team, Sage Mathematics Software, 2013.

H. Satô, Gaussian Measure on a Banach Space and Abstract Winer Measure, Analyse Hilbertienne. Hermann, pp.65-81, 1969.
DOI : 10.2140/pjm.1957.7.885

G. Spöck and J. Pilz, Spatial sampling design and covariance-robust minimax prediction based on convex design ideas, Stochastic Environmental Research and Risk Assessment, vol.11, issue.5, pp.463-482, 2010.
DOI : 10.1007/s00477-009-0334-y

C. Schwab and R. A. Todor, Karhunen???Lo??ve approximation of random fields by generalized fast multipole methods, Journal of Computational Physics, vol.217, issue.1, pp.100-122, 2006.
DOI : 10.1016/j.jcp.2006.01.048

J. Sacks, W. J. Welch, T. J. Mitchell, and H. P. Wynn, Design and Analysis of Computer Experiments, Statistical Science, vol.4, issue.4, pp.409-423, 1989.
DOI : 10.1214/ss/1177012413

J. Sacks and D. Ylvisaker, Designs for regression problems with correlated errors. The Annals of Mathematical Statistics, pp.66-89, 1966.
DOI : 10.1214/aoms/1177699599
URL : http://projecteuclid.org/download/pdf_1/euclid.aoms/1177699599

G. Wahba, Spline Models for Observational Data, SIAM, vol.59, 1990.
DOI : 10.1137/1.9781611970128