G. C. Orsak and B. Aazhang, A class of optimum importance sampling strategies, Information Sciences, vol.84, issue.1-2, p.139160, 1995.
DOI : 10.1016/0020-0255(94)00116-S

D. P. Kroese and R. Y. Rubinstein, Monte Carlo methods, Wiley Interdisciplinary Reviews: Computational Statistics, vol.8, issue.1, p.4858, 2012.
DOI : 10.1002/wics.194

J. Richard and W. Zhang, Ecient high-dimensional importance sampling, Journal of Econometrics, vol.141, issue.2, p.13851411, 2007.

S. Engelund and R. Rackwitz, A benchmark study on importance sampling techniques in structural reliability, Structural Safety, vol.12, issue.4, p.255276, 1993.
DOI : 10.1016/0167-4730(93)90056-7

H. Kahn and T. Harris, Estimation of particle transmission by random sampling, Appl. Math. Ser, vol.12, p.2730, 1951.

M. Rosenbluth and A. Rosenbluth, Monte Carlo Calculation of the Average Extension of Molecular Chains, The Journal of Chemical Physics, vol.23, issue.2, p.356359, 1955.
DOI : 10.1063/1.1741967

P. , D. Moral, and F. Formulae, Genealogical and Interacting Particle Systems with Applications. Probability and its Applications, 2004.

F. Cerou, P. Del-moral, T. Furon, and A. Guyader, Sequential Monte-Carlo for rare event estimation, Statistics and Computing, vol.22, p.795808, 2012.
URL : https://hal.archives-ouvertes.fr/inria-00584352

Z. Yan-gang and O. Tetsuro, A general procedure for rst/second-order reliabilitymethod, Structural Safety, p.95112, 1999.

R. Bjerager, Methods for Structural Reliability Computations, p.89136, 1991.
DOI : 10.1007/978-3-7091-2616-5_3

H. Madsen, S. Krenk, and N. C. Lind, Methods of structural safety, 1986.

R. Fisher and L. Tippett, On the estimation of the frequency distributions of the largest or smallest member of a sample, Proceedings of the Cambridge Philosophical Society, 1928.

M. Leadbetter, G. Lindgren, and H. Rootzén, Extremes and related properties of random sequences and series, 1983.

M. Leadbetter and H. Rootzén, Extremal Theory for Stochastic Processes, The Annals of Probability, vol.16, issue.2, p.431478, 1988.
DOI : 10.1214/aop/1176991767

A. Saltelli, S. Tarantola, F. Campolongo, and M. Ratto, Sensitivity analysis in practice: A guide to assessing scientic models, 2004.
DOI : 10.1002/0470870958

M. Lemaire, A. Chateauneuf, and J. Mitteau, Structural reliability, 2009.
DOI : 10.1002/9780470611708

M. Munoz-zuniga, J. Garnier, E. Remy, and E. De-rocquigny, Adaptive directional stratification for controlled estimation of the probability of a rare event, Reliability Engineering & System Safety, vol.96, issue.12, pp.1691-1712, 2011.
DOI : 10.1016/j.ress.2011.06.016

J. Morio, Inuence of input pdf parameters of a model on a failure probability estimation, Simulation Modelling Practice and Theory, p.22442255, 2011.

P. , D. Moral, P. Hu, and L. Wu, On the concentration properties of interacting particle processes, p.225389, 2012.
URL : https://hal.archives-ouvertes.fr/inria-00607684

N. Chopin, P. E. Jacob, and O. Papaspiliopoulos, SMC 2 : an ecient algorithm for sequential analysis of state space models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2012.

C. Vergé, C. Dubarry, P. , and E. Moulines, On parallel implementation of sequential Monte Carlo methods: the island particle model, Statistics and Computing, vol.90, issue.420, 2013.
DOI : 10.1007/s11222-013-9429-x

C. Andrieu, A. Doucet, and R. Holenstein, Particle Markov chain Monte Carlo methods, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.50, issue.3, p.269342, 2010.
DOI : 10.1111/j.1467-9868.2009.00736.x

A. J. Keane and P. B. Nair, Computational Approaches for Aerospace Design, 2005.
DOI : 10.1002/0470855487

I. Banerjee, S. Pal, and S. Maiti, Computationally efficient black-box modeling for feasibility analysis, Computers & Chemical Engineering, vol.34, issue.9, pp.1515-1521, 2010.
DOI : 10.1016/j.compchemeng.2010.02.016

A. Grancharova, J. Kocijan, and T. A. Johansen, Explicit output-feedback nonlinear predictive control based on black-box models, Engineering Applications of Artificial Intelligence, vol.24, issue.2, p.388397, 2011.
DOI : 10.1016/j.engappai.2010.10.009

V. Kreinovich and S. Ferson, A new Cauchy-based black-box technique for uncertainty in risk analysis, Reliability Engineering & System Safety, vol.85, issue.1-3, p.267279, 2004.
DOI : 10.1016/j.ress.2004.03.016

K. Worden, C. Wong, U. Parlitz, A. Hornstein, D. Engster et al., Identication of pre-sliding and sliding friction dynamics: Grey box and black-box 23

J. A. Bucklew, Introduction to Rare Event Simulation, 2004.
DOI : 10.1007/978-1-4757-4078-3

R. Rubinstein and D. Kroese, The Cross-Entropy Method : A Unied Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning (Information Science and Statistics)

P. Zhang, Nonparametric Importance Sampling, Journal of the American Statistical Association, vol.55, issue.435, p.12451253, 1996.
DOI : 10.1214/aos/1176343541

Z. I. Botev and D. P. Kroese, Ecient Monte-Carlo simulation via the generalized splitting method, Statistics and Computing, vol.22, issue.1, p.116, 2012.

L. Tierney, Markov Chains for Exploring Posterior Distributions, The Annals of Statistics, vol.22, issue.4, p.17011762, 1994.
DOI : 10.1214/aos/1176325750

A. Nataf, Distribution des distributions dont les marges sont données, Comptes rendus de l, Académie des Sciences, vol.225, p.4243, 1962.

L. Pei-ling and A. D. Kiureghian, Optimization algorithms for structural reliability, Structural Safety, p.161177, 1991.

R. Lebrun and A. Dutfoy, An innovating analysis of the Nataf transformation from the copula viewpoint, Probabilistic Engineering Mechanics, p.312320, 2009.
DOI : 10.1016/j.probengmech.2008.08.001

M. Rosenblatt, Remarks on a Multivariate Transformation, The Annals of Mathematical Statistics, vol.23, issue.3, p.470472, 1952.
DOI : 10.1214/aoms/1177729394

R. Lebrun and A. Dutfoy, A generalization of the Nataf transformation to distributions with elliptical copula, Probabilistic Engineering Mechanics, p.172178, 2009.
DOI : 10.1016/j.probengmech.2008.05.001

J. Morio, Non-parametric adaptive importance sampling for the probability estimation of a launcher impact position, Reliability Engineering & System Safety, vol.96, issue.1, pp.178-183, 2011.
DOI : 10.1016/j.ress.2010.08.006

I. Sobol and S. Kuchereko, Sensitivity estimates for non linear mathematical models, Mathematical Modelling and Computationnal Experiments, vol.1, p.407414, 1993.