L. Bastos and A. O-'hagan, Diagnostics for Gaussian Process Emulators, Technometrics, vol.51, issue.4, pp.425-438, 2009.
DOI : 10.1198/TECH.2009.08019

R. Bates, R. Kenett, and D. Steinberg, Achieving Robust Design from Computer Simulations, Quality Technology & Quantitative Management, vol.3, issue.2, pp.161-177, 2006.
DOI : 10.1080/16843703.2006.11673107

P. Bratley and B. Fox, ALGORITHM 659: implementing Sobol's quasirandom sequence generator, ACM Transactions on Mathematical Software, vol.14, issue.1, pp.88-100, 1988.
DOI : 10.1145/42288.214372

M. Goldstein, Bayesian analysis for complex physical systems modelled by computer simulations: current status and future challenges, p.IFIP, 2011.

M. Goldstein and D. Wooff, Bayes Linear Statistics, Theory and Methods, 2007.

J. Halton, On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals, Numerische Mathematik, vol.38, issue.1, pp.84-90, 1960.
DOI : 10.1007/BF01386213

L. Hascoët, R. M. Greborio, and V. Pascual, Computing adjoints by automatic differentiation with Tapenade, Ecole INRIA- CEA-EDF Problemes non-lineaires appliques, 2005.

M. Johnson, L. Moore, and D. Ylvisaker, Minimax and maximin distance designs, Journal of Statistical Planning and Inference, vol.26, issue.2, pp.131-148, 1990.
DOI : 10.1016/0378-3758(90)90122-B

M. Kennedy and A. O-'hagan, Bayesian calibration of computer models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.63, issue.3, pp.425-464, 2001.
DOI : 10.1111/1467-9868.00294

Y. Lim, J. Sacks, W. Studden, and W. Welch, Design and analysis of computer experiments when the output is highly correlated over the input space, Canadian Journal of Statistics, vol.38, issue.1, pp.109-126, 2002.
DOI : 10.2307/3315868

M. E. Maltrud and J. Mcclean, An eddy resolving global 1/10?? ocean simulation, Ocean Modelling, vol.8, issue.1-2, pp.31-54, 2005.
DOI : 10.1016/j.ocemod.2003.12.001

M. Mckay, R. Beckman, and W. 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.

J. Oakley and A. O-'hagan, Probabilistic sensitivity analysis of complex models: a Bayesian approach, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.34, issue.3, pp.751-769, 2004.
DOI : 10.1214/ss/1009213004

A. Owen, Orthogonal arrays for computer experiments, integration and visualization, Stat Sinica, vol.2, issue.2, pp.439-452, 1992.

C. Rasmussen and C. Williams, Gaussian Processes in Machine Learning, 2006.
DOI : 10.1162/089976602317250933
URL : http://hdl.handle.net/11858/00-001M-0000-0013-F365-A

A. Saltelli, K. Chan, and E. Scott, Sensitivity Analysis: Gauging the Worth of Scientific Models, 2000.

T. Santner, B. Williams, and W. Notz, The Design and Analysis of Computer Experiments, 2003.
DOI : 10.1007/978-1-4757-3799-8

J. Shakun and A. Carlson, A global perspective on Last Glacial Maximum to Holocene climate change, Quaternary Science Reviews, vol.29, issue.15-16, pp.1801-1836, 2010.
DOI : 10.1016/j.quascirev.2010.03.016

L. Sobol, On the distribution of points in a cube and the approximate evaluation of integrals, USSR Computational Mathematics and Mathematical Physics, vol.7, issue.4, pp.86-112, 1967.
DOI : 10.1016/0041-5553(67)90144-9

I. Vernon, M. Goldstein, and R. G. Bower, Galaxy formation: a Bayesian uncertainty analysis, Bayesian Analysis, vol.5, issue.4, pp.619-669, 2010.
DOI : 10.1214/10-BA524

D. Wilkinson, Stochastic Modelling for Systems Biology, 2006.