  The Stein's method is a popular method used to derive upper-bounds of
distances between probability distributions. It can be viewed, in certain of
its formulations, as an avatar of the semi-group or of the smart-path method
used commonly in Gaussian analysis. We show how this procedure can be enriched
by Malliavin calculus leading to a functional approach valid in infinite
dimensional spaces.
