Combining expert opinions in prior elicitation, Bayesian Analysis, vol.7, issue.3, pp.503-532, 2012. ,
DOI : 10.1214/12-ba717
URL : https://hal.archives-ouvertes.fr/hal-01004440
An introduction to mcmc for machine learning, Machine learning, vol.50, issue.1-2, pp.5-43, 2003. ,
, Maximin design on non hypercube domain and kernel interpolation, 2010.
DOI : 10.1016/j.sbspro.2010.05.137
URL : https://hal.archives-ouvertes.fr/inria-00638728
Calibration and improved prediction of computer models by universal kriging, Nuclear Science and Engineering, vol.176, issue.1, pp.81-97, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01020594
A framework for validation of computer models, Technometrics, vol.49, issue.2, pp.138-154, 2007. ,
Convergence rates of efficient global optimization algorithms, Journal of Machine Learning Research, vol.12, pp.2879-2904, 2011. ,
CaliCo: Code Calibration in a Bayesian Framework, 2018. ,
Explaining the gibbs sampler, The American Statistician, vol.46, issue.3, pp.167-174, 1992. ,
DOI : 10.2307/2685208
URL : http://www.stat.duke.edu/~scs/Courses/Stat376/Papers/Basic/CasellaGeorge1992.pdf
R6: Classes with Reference Semantics, 2017. ,
A statistical method for tuning a computer code to a data base, Computational statistics & data analysis, vol.37, issue.1, pp.77-92, 2001. ,
Bayesian forecasting for complex systems using computer simulators, Journal of the American Statistical Association, vol.96, issue.454, pp.717-729, 2001. ,
DOI : 10.1198/016214501753168370
Cassie (1993). statistics for spatial data, vol.900, 1993. ,
Bayesian prediction of deterministic functions, with applications to the design and analysis of computer experiments, Journal of the American Statistical Association, vol.86, issue.416, pp.953-963, 1991. ,
Global sensitivity analysis with dependence measures, Journal of Statistical Computation and Simulation, vol.85, issue.7, pp.1283-1305, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-00903283
Contributions statistiques au calage et à la validation des codes de calcul, 2015. ,
Adaptive numerical designs for the calibration of computer codes, SIAM/ASA Journal on Uncertainty Quantification, vol.6, issue.1, pp.151-179, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01784898
Numerical studies of space-filling designs: optimization of latin hypercube samples and subprojection properties, Journal of Simulation, vol.7, issue.4, pp.276-289, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00848240
Bayesian model selection for the validation of computer codes, Quality and Reliability Engineering International, vol.32, issue.6, pp.2043-2054, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01531713
New improvements in the use of dependence measures for sensitivity analysis and screening, Journal of Statistical Computation and Simulation, vol.86, issue.15, pp.3038-3058, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01090475
A new method to determine the optimum load of a real solar cell using the lambert w-function, Solar Energy Materials and Solar Cells, vol.92, issue.12, pp.1566-1569, 2008. ,
Solar engineering of thermal processes, 2013. ,
DOI : 10.1002/9781118671603
Minimal spanning tree: A new approach for studying order and disorder, Physical Review B, vol.34, issue.5, p.3528, 1986. ,
DOI : 10.1103/physrevb.34.3528
Rcpp: Seamless R and C++ Integration, 2018. ,
DOI : 10.18637/jss.v040.i08
URL : https://www.jstatsoft.org/index.php/jss/article/view/v040i08/v40i08.pdf
Analyse de sensibilité et exploration de modèles: application aux sciences de la nature et de l'environnement, 2013. ,
Design and modeling for computer experiments, 2005. ,
DOI : 10.1201/9781420034899
DiceDesign: Designs of Computer Experiments, 2015. ,
Minimum spanning tree: A new approach to assess the quality of the design of computer experiments, Chemometrics and intelligent laboratory systems, vol.97, issue.2, pp.164-169, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-00409737
Bayesian data analysis, 1995. ,
Inference from iterative simulation using multiple sequences. Statistical science, pp.457-472, 1992. ,
DOI : 10.1214/ss/1177011136
URL : https://doi.org/10.1214/ss/1177011136
, mvtnorm: Multivariate Normal and t Distributions, 2018.
Multiples metamodeles pour l'approximation et l'optimisation de fonctions numeriques multivariables, 2009. ,
URL : https://hal.archives-ouvertes.fr/tel-00772384
Jointly robust prior for gaussian stochastic process in emulation, calibration and variable selection, 2018. ,
RobustCalibration: Robust Calibration of Imperfect Mathematical Models, 2018. ,
Scaled gaussian stochastic process for computer model calibration and prediction, 2017. ,
DOI : 10.1137/17m1159890
URL : http://arxiv.org/pdf/1707.08215
An adaptive metropolis algorithm, Bernoulli, vol.7, issue.2, pp.223-242, 2001. ,
DOI : 10.2307/3318737
A bayesian analysis of kriging, Technometrics, vol.35, issue.4, pp.403-410, 1993. ,
DOI : 10.2307/1270273
approximator: Bayesian prediction of complex computer codes, 2013. ,
BACCO: Bayesian Analysis of Computer Code Output (BACCO). R package version 2, pp.0-9, 2013. ,
calibrator: Bayesian calibration of complex computer codes, 2013. ,
emulator: Bayesian emulation of computer programs, 2014. ,
Monte carlo sampling methods using markov chains and their applications, Biometrika, vol.57, issue.1, pp.97-109, 1970. ,
DOI : 10.2307/2334940
Assessment of uncertainty in computer experiments from universal to bayesian kriging, Applied Stochastic Models in Business and Industry, vol.25, issue.2, pp.99-113, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-00407651
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
URL : http://www.stat.duke.edu/~fei/samsi/Readings/DHigdon/nedd4.pdf
Combining field data and computer simulations for calibration and prediction, SIAM Journal on Scientific Computing, vol.26, issue.2, pp.448-466, 2004. ,
DOI : 10.1137/s1064827503426693
A first course in Bayesian statistical methods, 2009. ,
FactoMineR: Multivariate Exploratory Data Analysis and Data Mining, 2018. ,
Exploratory multivariate analysis by example using R, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-00566638
Modeling and simulation of photovoltaic (pv) system during partial shading based on a two-diode model, Simulation Modelling Practice and Theory, vol.19, issue.7, pp.1613-1626, 2011. ,
An efficient algorithm for constructing optimal design of computer experiments, ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp.545-554, 2003. ,
DOI : 10.1016/j.jspi.2004.02.014
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
Efficient global optimization of expensive black-box functions, Journal of Global optimization, vol.13, issue.4, pp.455-492, 1998. ,
Non-informative priors and modelization by mixtures, 2016. ,
URL : https://hal.archives-ouvertes.fr/tel-01491350
Application of hardy's multiquadric interpolation to hydrodynamics, 1985. ,
Supplementary details on bayesian calibration of computer. rap. tech., university of nottingham, Statistics Section, 2001. ,
Bayesian calibration of computer models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.63, issue.3, pp.425-464, 2001. ,
Bayesian treed calibration: an application to carbon capture with ax sorbent, Journal of the American Statistical Association, vol.112, issue.517, pp.37-53, 2017. ,
A statistical approach to some basic mine valuation problems on the witwatersrand, Journal of the Southern African Institute of Mining and Metallurgy, vol.52, issue.6, pp.119-139, 1951. ,
Multi-fidelity Gaussian process regression for computer experiments, 2013. ,
URL : https://hal.archives-ouvertes.fr/tel-00866770
Modularization in bayesian analysis, with emphasis on analysis of computer models, Bayesian Analysis, vol.4, issue.1, pp.119-150, 2009. ,
, GPfit: Gaussian Processes Modeling, 2015.
Mise en oeuvre et exploitation du métamodèle processus gaussien pour l'analyse de modèles numériques-application à un code de transport hydrogéologique, 2008. ,
Calculations of sobol indices for the gaussian process metamodel, Reliability Engineering & System Safety, vol.94, issue.3, pp.742-751, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-00239494
Spatial variation: Meddelanden fran statens skogsforskningsinstitut, Lecture Notes in Statistics, vol.36, p.21, 1960. ,
Principles of geostatistics, Economic geology, vol.58, issue.8, pp.1246-1266, 1963. ,
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. ,
RobustGaSP: Robust Gaussian Stochastic Process Emulation, 2018. ,
Equation of state calculations by fast computing machines, The journal of chemical physics, vol.21, issue.6, pp.1087-1092, 1953. ,
On metropolis-hastings algorithms with delayed rejection, Metron, vol.59, issue.3-4, pp.231-241, 2001. ,
Factorial sampling plans for preliminary computational experiments, Technometrics, vol.33, issue.2, pp.161-174, 1991. ,
Exploratory designs for computational experiments, Journal of statistical planning and inference, vol.43, issue.3, pp.381-402, 1995. ,
Probabilistic sensitivity analysis of complex models: a bayesian approach, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.66, issue.3, pp.751-769, 2004. ,
Orthogonal arrays for computer experiments, integration and visualization, Statistica Sinica, pp.439-452, 1992. ,
SAVE: Bayesian Emulation, Calibration and Validation of Computer Models, 2017. ,
Liii. on lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine, Journal of Science, vol.2, issue.11, pp.559-572, 1901. ,
Analytical model of mismatched photovoltaic fields by means of lambert w-function. Solar energy materials and solar cells, vol.91, pp.1652-1657, 2007. ,
Forecasting photovoltaic array power production subject to mismatch losses, Solar Energy, vol.84, issue.7, pp.1301-1309, 2010. ,
DOI : 10.1016/j.solener.2010.04.009
URL : https://hal.archives-ouvertes.fr/hal-00488003
Bayesian calibration of inexact computer models, Journal of the American Statistical Association, vol.112, issue.519, pp.1274-1285, 2017. ,
DOI : 10.1080/01621459.2016.1211016
, coda: Output Analysis and Diagnostics for MCMC, 2016.
Design of computer experiments: space filling and beyond, Statistics and Computing, vol.22, issue.3, pp.681-701, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00685876
Gaussian processes in machine learning, Advanced lectures on machine learning, pp.63-71, 2004. ,
DOI : 10.1007/978-3-540-28650-9_4
URL : http://mlg.eng.cam.ac.uk/pub/pdf/Ras04.pdf
Méthodes de Monte Carlo par chaînes de Markov, Economica, 1996. ,
The Bayesian choice: from decision-theoretic foundations to computational implementation, 2007. ,
, , 2013.
, Monte Carlo statistical methods
Weak convergence and optimal scaling of random walk metropolis algorithms. The annals of applied probability, vol.7, pp.110-120, 1997. ,
DOI : 10.1214/aoap/1034625254
URL : https://doi.org/10.1214/aoap/1034625254
Quantifying uncertainty in an industrial approach: an emerging consensus in an old epistemological debate, SAPI EN. S. Surveys and Perspectives Integrating Environment and Society, issue.2, 2009. ,
DiceKriging: Kriging Methods for Computer Experiments, 2015. ,
Dicekriging, diceoptim: Two r packages for the analysis of computer experiments by kriging-based metamodelling and optimization, Journal of Statistical Software, vol.51, issue.1, p.54, 2012. ,
URL : https://hal.archives-ouvertes.fr/emse-00741762
Design and analysis of computer experiments, Statistical science, pp.409-423, 1989. ,
Making best use of model evaluations to compute sensitivity indices, Computer physics communications, vol.145, issue.2, pp.280-297, 2002. ,
DOI : 10.1016/s0010-4655(02)00280-1
, Sensitivity analysis, vol.1, 2000.
URL : https://hal.archives-ouvertes.fr/inria-00386559
Sensitivity analysis in practice: a guide to assessing scientific models, 2004. ,
The design and analysis of computer experiments, 2013. ,
Convergence of unsymmetric kernel-based meshless collocation methods, SIAM Journal on Numerical Analysis, vol.45, issue.1, pp.333-351, 2007. ,
On sensitivity estimation for nonlinear mathematical models, Matematicheskoe modelirovanie, vol.2, pp.112-118, 1990. ,
Sensitivity estimates for nonlinear mathematical models, Mathematical modelling and computational experiments, vol.1, issue.4, pp.407-414, 1993. ,
Large sample properties of simulations using latin hypercube sampling, Technometrics, vol.29, issue.2, pp.143-151, 1987. ,
Interpolation of spatial data: some theory for kriging, 2012. ,
Global sensitivity analysis using polynomial chaos expansions, Reliability Engineering & System Safety, vol.93, issue.7, pp.964-979, 2008. ,
URL : https://hal.archives-ouvertes.fr/hal-01432217
Orthogonal array-based latin hypercubes, Journal of the American statistical association, vol.88, issue.424, pp.1392-1397, 1993. ,
A cell-to-module-to-array detailed model for photovoltaic panels, Solar energy, vol.86, issue.9, pp.2695-2706, 2012. ,
Efficient calibration for imperfect computer models, The Annals of Statistics, vol.43, issue.6, pp.2331-2352, 2015. ,
A theoretical framework for calibration in computer models: parametrization, estimation and convergence properties, SIAM/ASA Journal on Uncertainty Quantification, vol.4, issue.1, pp.767-795, 2016. ,
Convergence properties of the expected improvement algorithm with fixed mean and covariance functions, Journal of Statistical Planning and inference, vol.140, issue.11, pp.3088-3095, 2010. ,
URL : https://hal.archives-ouvertes.fr/hal-00217562
An algorithm for fast optimal latin hypercube design of experiments, International journal for numerical methods in engineering, vol.82, issue.2, pp.135-156, 2010. ,
ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics, 2016. ,
A frequentist approach to computer model calibration, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.79, issue.2, pp.635-648, 2017. ,