B. Allen, Neighboring information and distributions of agents' characteristics under uncertainty, Journal of Mathematical Economics, vol.12, issue.1, pp.63-101, 1983.
DOI : 10.1016/0304-4068(83)90051-4

L. Andrieu, Optimisation sous contrainte en probabilité, Thèse de doctorat, ´ Ecole Nationale des Ponts et Chaussées, 2004.

Z. Artstein, Probing for information in two-stage stochastic programming and the associated consistency Gains and costs of information in stochastic programming, Stochastic Stochastics Rep. Oper. Res, vol.5, issue.36 85, pp.41-63, 1991.

J. Aubin and H. Frankowska, Set-valued analysis, 1990.
DOI : 10.1007/978-1-4612-1576-9_5

V. Barbu, . Th, and . Precupanu, Convexity and optimization in banach spaces, 1986.

K. Barty, ContributionsàContributionsà la discrétisation des contraintes de mesurabilité pour lesprobì emes d'optimisation stochastique, Thèse de doctorat, ´ Ecole Nationale des Ponts et Chaussées, 2004.

K. Barty, J. Roy, and C. Strugarek, A perturbed gradient algorithm in Hilbert spaces, Optimization Online http ://www.optimization-online.org/DB HTML A stochastic gradient type algorithm for closed loop problems, submitted, Temporal difference learning with kernels for pricing american-style options, p.5, 1133.

R. Bellman and S. E. Dreyfus, Functional approximations and dynamic programming, Math tables and other aides to computation 13, pp.247-251, 1959.

A. Benvéniste, M. Métivier, and P. Priouret, Adaptive algorithms and stochastic approximation, 1990.
DOI : 10.1007/978-3-642-75894-2

H. Berliocchi and J. Lasry, Nouvelles applications des mesures paramétrées, C. R. Acad. Sci, vol.274, pp.1623-1626, 1972.

P. Bernhard and G. Cohen, Commande optimale, décentralisation et jeux dynamiques, 1976.

D. P. Bertsekas and S. E. Shreve, Stochastic optimal control : the discrete-time case, Athena Scientific, 1996.

D. P. Bertsekas and J. N. Tsitsiklis, Gradient Convergence in Gradient methods with Errors, Neuro-Dynamic Programming, pp.627-642, 1996.
DOI : 10.1137/S1052623497331063

J. F. Bonnans and A. Shapiro, Perturbation analysis of optimization problems, 2000.
DOI : 10.1007/978-1-4612-1394-9

E. S. Boylan, Equiconvergence of Martingales, The Annals of Mathematical Statistics, vol.42, issue.2, pp.552-559, 1971.
DOI : 10.1214/aoms/1177693405

P. Carpentier, G. Cohen, and J. Culioli, Stochastic optimal control and decomposition-coordination methods -Part I : Theory, Recent Developments in Optimization Stochastic optimal control and decomposition-coordination methods -Part II : Applications, Recent Developments in Optimization, Lecture Notes in Economics and Mathematical Systems, pp.72-87, 1995.

X. Chen and H. White, Asymptotic properties of some projection-based Robbins-Monro procedures in a Hilbert space, Stud. Nonlinear Dyn. Econom, vol.6, pp.1-53, 2002.

G. Cohen, Optimization by decomposition and coordination: A unified approach, IEEE Transactions on Automatic Control, vol.23, issue.2, pp.222-232, 1978.
DOI : 10.1109/TAC.1978.1101718

G. Cohen and J. Culioli, Decomposition Coordination Algorithms for Stochastic Optimization, Optimisation stochastique sous contraintes en espérance, Rapport interne Centre Automatique et Systèmes, Ecole des Mines de Paris, pp.1372-1403, 1990.

K. D. Cotter, Similarity of information and behavior with a pointwise convergence topology Convergence of information, random variables and noise, J. Math. Econ. J. Math. Econ, vol.35, issue.15 16, pp.25-38, 1986.

J. Culioli, Algorithmes de décomposition/coordination en optimisation stochastique, 1987.

D. P. De-farias and B. Van-roy, The Linear Programming Approach to Approximate Dynamic Programming, Operations Research, vol.51, issue.6, pp.850-856, 2003.
DOI : 10.1287/opre.51.6.850.24925

P. Deheuvels, Sur l'estimation séquentielle de la densité, Estimation séquentielle de la densité, pp.1119-1121, 1973.

F. Delebecque and J. Quadrat, Contribution of stochastic control singular perturbation averaging and team theories to an example of large-scale systems: Management of hydropower production, IEEE Transactions on Automatic Control, vol.23, issue.2, pp.209-222, 1978.
DOI : 10.1109/TAC.1978.1101726

L. Devroye, A course in density estimation, 1987.

R. M. Dudley, Real analysis and probability, 2002.
DOI : 10.1017/CBO9780511755347

M. Duflo, Algorithmes stochastiques, Random iterative models, 1996.

J. Dupà-cová, N. Gröwe-kuska, and W. Römisch, Scenario reduction in stochastic programming, Mathematical Programming, vol.95, issue.3, pp.493-511, 2003.
DOI : 10.1007/s10107-002-0331-0

J. Dupà-cová and R. J. Wets, Asymptotic Behavior of Statistical Estimators and of Optimal Solutions of Stochastic Optimization Problems, The Annals of Statistics, vol.16, issue.4, pp.1517-1549, 1988.
DOI : 10.1214/aos/1176351052

Y. Ermoliev, V. Norkin, and R. J. Wets, The Minimization of Semicontinuous Functions: Mollifier Subgradients, SIAM Journal on Control and Optimization, vol.33, issue.1, pp.149-167, 1995.
DOI : 10.1137/S0363012992238369

O. Fiedler and W. Römisch, Stability in multistage stochastic programming, Annals of Operations Research, vol.5, issue.1, pp.79-93, 1995.
DOI : 10.1007/BF02031701

R. Fortet and E. Mourier, Convergence de la r??partition empirique vers la r??partition th??orique, Annales scientifiques de l'??cole normale sup??rieure, vol.70, issue.3, pp.267-285, 1953.
DOI : 10.24033/asens.1013

P. Glasserman, Monte-Carlo methods in financial engineering, 2003.
DOI : 10.1007/978-0-387-21617-1

L. Greengard and J. Strain, The Fast Gauss Transform, SIAM Journal on Scientific and Statistical Computing, vol.12, issue.1, pp.79-94, 1991.
DOI : 10.1137/0912004

H. Heitsch and W. Römisch, Scenario reduction algorithms in stochastic programming Scenario tree modelling for multistage stochastic programs, Comput. Optim. Appl, issue.24, pp.187-206, 2003.

H. Heitsch, W. Römisch, and C. Strugarek, Stability of multistage stochastic programs Mathematics for key technologies, SIAM J. Optim.), issue.255, 2005.

J. Hiriart-urruty, Algorithmes stochastiques de r???solution d'???quations et d'in???quations variationnelles, Zeitschrift f???r Wahrscheinlichkeitstheorie und Verwandte Gebiete, vol.28, issue.3, pp.167-186, 1975.
DOI : 10.1007/BF00534962

C. C. Holt, F. Modigliani, and H. A. Simon, A Linear Decision Rule for Production and Employment Scheduling, Management Science, vol.2, issue.1, 1955.
DOI : 10.1287/mnsc.2.1.1

D. N. Hoover, Convergence in distribution and Skorokhod Convergence for the general theory of processes, Probability Theory and Related Fields, vol.1, issue.3, pp.239-259, 1991.
DOI : 10.1007/BF01198786

J. Kiefer and J. Wolfowitz, Stochastic Estimation of the Maximum of a Regression Function, The Annals of Mathematical Statistics, vol.23, issue.3, pp.462-466, 1952.
DOI : 10.1214/aoms/1177729392

H. Kudo, A Note on the Strong Convergence of $\Sigma$-Algebras, The Annals of Probability, vol.2, issue.1, pp.76-83, 1974.
DOI : 10.1214/aop/1176996752

T. L. Lai, Stochastic Approximation, Ann. Stat, issue.2, pp.31-391, 2003.

M. B. Nevel-'son and R. Z. , Has'minskii, Stochastic approximation and recursive estimation, 1973.

J. Neveu, Note on the Tightness of the Metric on the Set of Complete Sub $\sigma$-Algebras of a Probability Space, The Annals of Mathematical Statistics, vol.43, issue.4, pp.1369-1371, 1972.
DOI : 10.1214/aoms/1177692492

E. Parzen, On estimating of a probability density and mode, Annals of Mathematical Statistics, vol.35, pp.1065-1076, 1962.

T. Pennanen, Epi-Convergent Discretizations of Multistage Stochastic Programs, Mathematics of Operations Research, vol.30, issue.1, pp.245-256, 2005.
DOI : 10.1287/moor.1040.0114

G. Ch, Pflug, Scenario tree generation for multiperiod financial optimization by optimal discretization, Math. Program, issue.89, pp.251-271, 2001.

B. T. Polyak and Y. Z. Tsypkin, Pseudogradient adaptation and training algorithms, Autom. Remote Control, vol.12, pp.83-94, 1973.

S. T. Rachev and W. Römisch, Quantitative Stability in Stochastic Programming: The Method of Probability Metrics, Mathematics of Operations Research, vol.27, issue.4, pp.792-818, 2002.
DOI : 10.1287/moor.27.4.792.304

P. Révész, Hilbert space and its application in the theory of learning processes, I. Robbins-Monro procedure in a Hilbert space How to apply the method of stochastic approximation in the non-parametric estimation of a regression function, Studia Sci. Math. Hungar. II., Studia Sci. Math. Hungar. Math. Operationsforsch. Statist. Ser. Statistics, vol.8, issue.8 1, pp.391-398, 1973.

H. Robbins and S. Monro, A Stochastic Approximation Method, The Annals of Mathematical Statistics, vol.22, issue.3, pp.400-407, 1951.
DOI : 10.1214/aoms/1177729586

H. Robbins and D. Siegmund, A convergence theorem for nonnegative almost supermartingales and some applications, Optimizing Methods in Statistics, pp.233-257, 1971.

S. M. Robinson, Analysis of Sample-Path Optimization, Mathematics of Operations Research, vol.21, issue.3, 1996.
DOI : 10.1287/moor.21.3.513

R. T. Rockafellar, Convex analysis, 1970.
DOI : 10.1515/9781400873173

R. T. Rockafellar and R. J. Wets, Stochastic convex programming : basic duality Stochastic convex programming : singular multipliers and extended duality, singular multipliers and duality Scenarios and policy aggregation in optimization under uncertainty Variational analysis, Pacific J. Math. Pacific J. Math. Math. Oper. Res, vol.62, issue.16 1, pp.173-195, 1976.

L. Rogge, Uniform Inequalities for Conditional Expectations, The Annals of Probability, vol.2, issue.3, pp.486-489, 1974.
DOI : 10.1214/aop/1176996664

W. Römisch, Stability of stochastic programs, Handbooks in Operations Research and Management Science, pp.483-554, 2003.

M. Rosenblatt, Remarks on Some Nonparametric Estimates of a Density Function, The Annals of Mathematical Statistics, vol.27, issue.3, pp.832-837, 1956.
DOI : 10.1214/aoms/1177728190

D. Salinger, A splitting algorithm for multistage stochastic programming with application to hydropower scheduling, 1997.

A. Shapiro, T. Homem-de, and . Mello, On the Rate of Convergence of Optimal Solutions of Monte Carlo Approximations of Stochastic Programs, SIAM Journal on Optimization, vol.11, issue.1, pp.70-86, 2000.
DOI : 10.1137/S1052623498349541

S. Smale and Y. Yao, Online learning algorithms, Preprint, www.tti-c.org, 2004.

J. Strain, The Fast Gauss Transform with Variable Scales, SIAM Journal on Scientific and Statistical Computing, vol.12, issue.5, pp.1131-1139, 1991.
DOI : 10.1137/0912059
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.36.2092

T. J. Wagner and C. T. Wolverton, Recursive estimates of probability densities, IEEE Trans. Syst. Man. Cybern, vol.5, p.307, 1969.

H. S. Witsenhausen, A counterexample in stochastic optimal control, SIAM Journal of Control, vol.2, issue.6, pp.149-160, 1968.

C. Yang, R. Duraiswami, N. Gumerov, and L. Davis, Improved fast gauss transform and efficient kernel density estimation, Proceedings Ninth IEEE International Conference on Computer Vision, pp.464-471, 2003.
DOI : 10.1109/ICCV.2003.1238383
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.137.2987

G. Yin and Y. M. Zhu, On H-valued Robbins-Monro processes, Journal of Multivariate Analysis, vol.34, issue.1, pp.116-140, 1990.
DOI : 10.1016/0047-259X(90)90064-O
URL : http://doi.org/10.1016/0047-259x(90)90064-o