N. Belaribi, F. Cuvelier, and F. Russo, A probabilistic algorithm approximating solutions of a singular PDE of porous media type, Monte Carlo Methods and Applications, vol.53, issue.9, pp.317-369, 2011.
DOI : 10.1214/aoms/1177696810

URL : https://hal.archives-ouvertes.fr/inria-00535806

N. Belaribi, F. Cuvelier, and F. Russo, Probabilistic and deterministic algorithms for space multidimensional irregular porous media equation, Stochastic Partial Differential Equations: Analysis and Computations, vol.53, issue.1, pp.3-62, 2013.
DOI : 10.1007/BFb0085169

URL : https://hal.archives-ouvertes.fr/hal-00723821

A. Bensoussan, S. P. Sethi, R. Vickson, and N. Derzko, Stochastic Production Planning with Production Constraints, SIAM Journal on Control and Optimization, vol.22, issue.6, pp.920-935, 1984.
DOI : 10.1137/0322060

D. P. Bertsekas and S. E. Shreve, Stochastic optimal control The discrete time case, Mathematics in Science and Engineering, vol.139, 1978.

M. Bossy, L. Fezoui, and S. Piperno, COMPARISON OF A STOCHASTIC PARTICLE METHOD AND A FINITE VOLUME DETERMINISTIC METHOD APPLIED TO BURGERS EQUATION, Monte Carlo Methods and Applications, vol.3, issue.2, pp.113-140, 1997.
DOI : 10.1515/mcma.1997.3.2.113

URL : https://hal.archives-ouvertes.fr/hal-00607773

M. Bossy and D. Talay, Convergence rate for the approximation of the limit law of weakly interacting particles: application to the Burgers equation, The Annals of Applied Probability, vol.6, issue.3, pp.818-861, 1996.
DOI : 10.1214/aoap/1034968229

URL : https://hal.archives-ouvertes.fr/inria-00074265

B. Bouchard and N. Touzi, Discrete-time approximation and Monte Carlo simulation of backward stochastic differential equations. Stochastic Process, Appl, vol.111, pp.175-206, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00103046

P. Cheridito, H. M. Soner, N. Touzi, and N. Victoir, Second-order backward stochastic differential equations and fully nonlinear parabolic PDEs, Communications on Pure and Applied Mathematics, vol.1627, issue.7, pp.1081-1110, 2007.
DOI : 10.1080/17442509208833749

URL : http://arxiv.org/abs/math/0509295

F. Delarue and S. Menozzi, An interpolated stochastic algorithm for quasi-linear PDEs, Mathematics of Computation, vol.77, issue.261, pp.125-158, 2008.
DOI : 10.1090/S0025-5718-07-02008-X

URL : https://hal.archives-ouvertes.fr/hal-00021967

E. Gobet, J. Lemor, and X. Warin, A regression-based Monte Carlo method to solve backward stochastic differential equations, The Annals of Applied Probability, vol.15, issue.3, pp.2172-2202, 2005.
DOI : 10.1214/105051605000000412

URL : http://doi.org/10.1214/105051605000000412

A. G. Gray and A. W. Moore, Nonparametric Density Estimation: Toward Computational Tractability, Proceedings of the 2003 SIAM International Conference on Data Mining, pp.203-211, 2003.
DOI : 10.1137/1.9781611972733.19

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.104.3984

P. Henry-labordère, Counterparty risk valuation: A marked branching diffusion approach Available at SSRN: http://ssrn, 2012.

P. Henry-labordère, N. Oudjane, X. Tan, N. Touzi, and X. Warin, Branching diffusion representation of semilinear pdes and Monte Carlo approximations, 2016.

P. Henry-labordère, X. Tan, and N. Touzi, A numerical algorithm for a class of BSDEs via the branching process. Stochastic Process, Appl, vol.124, issue.2, pp.1112-1140, 2014.

B. Jourdain and S. Méléard, Propagation of chaos and fluctuations for a moderate model with smooth initial data, Annales de l'Institut Henri Poincare (B) Probability and Statistics, vol.34, issue.6, pp.727-766, 1998.
DOI : 10.1016/S0246-0203(99)80002-8

P. E. Kloeden and E. Platen, Numerical solution of stochastic differential equations, Applications of Mathematics, vol.23, 1992.

L. Cavil, N. Oudjane, and F. Russo, Particle system algorithm and chaos propagation related to a non-conservative McKean type stochastic differential equations. Stochastics and Partial Differential Equations: Analysis and Computation, pp.1-37, 2016.

A. L. Cavil, N. Oudjane, and F. Russo, Probabilistic representation of a class of non-conservative nonlinear partial differential equations, ALEA Lat. Am. J. Probab. Math. Stat, vol.13, issue.2, pp.1189-1233, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01241701

A. L. Cavil, N. Oudjane, and F. Russo, Forward Feynman-Kac type representation for semilinear nonconservative partial differential equations, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01586861

E. Pardoux, Backward Stochastic Differential Equations and Viscosity Solutions of Systems of Semilinear Parabolic and Elliptic PDEs of Second Order, Stochastic analysis and related topics, pp.79-127, 1996.
DOI : 10.1007/978-1-4612-2022-0_2

É. Pardoux and S. G. Peng, Adapted solution of a backward stochastic differential equation, Systems & Control Letters, vol.14, issue.1, pp.55-61, 1990.
DOI : 10.1016/0167-6911(90)90082-6

E. Pardoux and A. Ra¸scanura¸scanu, Stochastic differential equations, Backward SDEs, Partial differential equations, 2014.
DOI : 10.1007/978-3-319-05714-9

URL : https://hal.archives-ouvertes.fr/hal-01108223

B. W. Silverman, Density estimation for statistics and data analysis. Monographs on Statistics and Applied Probability, 1986.