In this talk, the convergence analysis of a class of weak approximations of 
solutions of stochastic differential equations is presented. This class includes 
recent approximations such as Kusuoka's moment similar families method and the 
Lyons-Victoir cubature on Wiener Space approach. It will be shown that the rate 
of convergence depends intrinsically on the smoothness of the chosen test 
function. For smooth functions (the required degree of smoothness depends on the 
order of the approximation), an equidistant partition of the time interval on 
which the approximation is sought is optimal. For functions that are less smooth 
(for example Lipschitz functions), the rate of convergence decays and the 
optimal partition is no longer equidistant. An asymptotic rate of convergence 
will also be presented for the Lyons-Victoir method. The analysis rests upon 
Kusuoka-Stroock's results on the smoothness of the distribution of the solution 
of a stochastic differential equation. Finally, the results will be applied to 
the numerical solution of the filtering problem.