  This article deals with the numerical resolution of backward stochastic
differential equations. Firstly, we consider a rather general case where the
filtration is generated by a Brownian motion and a Poisson random measure. We
provide a simulation algorithm based on iterative regressions on function
bases, which coefficients are evaluated using Monte Carlo simulations. We state
fully explicit error bounds. Secondly, restricting to the case of a Brownian
filtration, we consider reflected BSDEs and adapt the previous algorithm to
that situation. The complexity of the algorithm is very competitive and allows
us to treat numerical results in dimension 10.
