N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, Equation of State Calculations by Fast Computing Machines, The Journal of Chemical Physics, vol.21, issue.6, pp.1087-1091, 1953.
DOI : 10.1063/1.1699114

H. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika, vol.57, issue.1, pp.97-109, 1970.
DOI : 10.1093/biomet/57.1.97

M. J. Bayarri, J. O. Berger, R. Paulo, J. Sacks, J. A. Cafeo et al., A Framework for Validation of Computer Models, Technometrics, vol.49, issue.2, pp.138-154, 2007.
DOI : 10.1198/004017007000000092

D. Higdon, J. Gattiker, B. Williams, and M. Rightley, Computer Model Calibration Using High-Dimensional Output, Journal of the American Statistical Association, vol.103, issue.482, pp.570-583, 2008.
DOI : 10.1198/016214507000000888

H. Haario, J. Saksman, and . Tamminen, An Adaptive Metropolis Algorithm, Bernoulli, vol.7, issue.2, pp.223-242, 2001.
DOI : 10.2307/3318737

P. J. Green and A. Mira, Delayed rejection in reversible jump Metropolis-Hastings, Biometrika, vol.88, issue.4, pp.1035-1053, 2001.
DOI : 10.1093/biomet/88.4.1035

H. Haario, M. Laine, A. Mira, and E. Saksman, DRAM: Efficient adaptive MCMC, Statistics and Computing, vol.18, issue.2, pp.339-354
DOI : 10.1007/s11222-006-9438-0

C. J. Ter-braak and J. Vrugt, Differential Evolution Markov Chain with snooker updater and??fewer chains, Statistics and Computing, vol.69, issue.4, pp.435-446, 2008.
DOI : 10.1007/s11222-008-9104-9

M. C. 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

J. A. Christen and C. Fox, Markov chain Monte Carlo Using an Approximation, Journal of Computational and Graphical Statistics, vol.14, issue.4, pp.795-810, 2005.
DOI : 10.1198/106186005X76983

X. Ma and N. Zabaras, An efficient Bayesian inference approach to inverse problems based on an adaptive sparse grid collocation method, Inverse Problems, vol.25, issue.3, 2009.
DOI : 10.1088/0266-5611/25/3/035013

T. Cui, C. Fox, and M. A. Sullivan, Bayesian calibration of a largescale geothermal reservoir model by a new adaptive delayed acceptance metropolis-hastings algorithm, Water Resources Research, vol.47, 2011.

E. Laloy, B. Rogiers, J. A. Vrugt, D. Mallants, and D. Jacques, Efficient posterior exploration of a high-dimensional groundwater model from two-stage Markov chain Monte Carlo simulation and polynomial chaos expansion, Water Resources Research, vol.37, issue.76, pp.2664-2682, 2013.
DOI : 10.1002/wrcr.20226

A. Tarantola, Inverse Problem Theory and Methods for Model Parameter Estimation, 2005.
DOI : 10.1137/1.9780898717921

C. J. Ter-braak, A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces, Statistics and Computing, vol.69, issue.3, pp.239-249, 2006.
DOI : 10.1007/s11222-006-8769-1

J. A. Vrugt, C. J. Ter-braak, C. G. Diks, D. Higdon, B. A. Robinson et al., Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling, International Journal of Nonlinear Sciences and Numerical Simulation, vol.10, issue.3, pp.273-290, 2009.
DOI : 10.1515/IJNSNS.2009.10.3.273

J. A. Vrugt, C. J. Ter-braak, H. V. Gupta, and B. A. Robinson, Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?, Stochastic Environmental Research and Risk Assessment, vol.48, issue.3, pp.1011-1026, 2008.
DOI : 10.1007/s00477-008-0274-y

M. Laine, Adaptative mcmc methods with applications in environmental and geophysical models, 2008.

E. Laloy and J. A. Vrugt, High-dimensional posterior exploration of hydrologic models using multiple-try dream (zs) and highperformance computing, Water Resources Research, vol.48, pp.10-1029, 2012.

Q. Duan, S. Sorooshian, and V. K. Gupta, Optimal use of the SCE-UA global optimization method for calibrating watershed models, Journal of Hydrology, vol.158, issue.3-4, pp.265-284, 1994.
DOI : 10.1016/0022-1694(94)90057-4

W. Li and A. Cirpka, Efficient geostatistical inverse methods for structured and unstructured grids, Water Resources Research, vol.34, issue.6, 2006.
DOI : 10.1029/2005WR004668

Y. Efendiev, T. Y. Hou, and W. Luo, Preconditioning Markov Chain Monte Carlo Simulations Using Coarse-Scale Models, SIAM Journal on Scientific Computing, vol.28, issue.2, pp.776-803, 2006.
DOI : 10.1137/050628568

Y. Marzouk and H. N. Najm, Dimensionality reduction and polynomial chaos acceleration of Bayesian inference in inverse problems, Journal of Computational Physics, vol.228, issue.6, 2009.
DOI : 10.1016/j.jcp.2008.11.024

A. Younes, P. Ackerer, R. Mose, and G. Chavent, A New Formulation of the Mixed Finite Element Method for Solving Elliptic and Parabolic PDE with Triangular Elements, Journal of Computational Physics, vol.149, issue.1, pp.148-167, 1998.
DOI : 10.1006/jcph.1998.6150

D. Zhang and Z. Lu, An efficient, high-order perturbation approach for flow in random porous media via Karhunen???Lo??ve and polynomial expansions, Journal of Computational Physics, vol.194, issue.2, 2004.
DOI : 10.1016/j.jcp.2003.09.015

K. K. Phoon, S. P. Huang, and S. T. Quek, Implementation of Karhunen???Loeve expansion for simulation using a wavelet-Galerkin scheme, Probabilistic Engineering Mechanics, vol.17, issue.3, pp.293-303, 2002.
DOI : 10.1016/S0266-8920(02)00013-9

A. Kaczmaryk and F. Delay, Improving dual-porosity-medium approaches to account for karstic flow in a fractured limestone. application to the automatic inversion of hydraulic interference tests, Journal of Hydrology, vol.347, 2007.

P. Ackerer and F. Delay, Inversion of a set of well-test interferences in a fractured limestone aquifer by using an automatic downscaling parameterization technique, Journal of Hydrology, vol.389, issue.1-2, pp.42-56, 2010.
DOI : 10.1016/j.jhydrol.2010.05.020
URL : https://hal.archives-ouvertes.fr/insu-00555338