  The problem of the definition and the estimation of generative models based
on deformable templates from raw data is of particular importance for modelling
non aligned data affected by various types of geometrical variability. This is
especially true in shape modelling in the computer vision community or in
probabilistic atlas building for Computational Anatomy (CA). A first coherent
statistical framework modelling the geometrical variability as hidden variables
has been given by Allassonni\`ere, Amit and Trouv\'e (JRSS 2006). Setting the
problem in a Bayesian context they proved the consistency of the MAP estimator
and provided a simple iterative deterministic algorithm with an EM flavour
leading to some reasonable approximations of the MAP estimator under low noise
conditions. In this paper we present a stochastic algorithm for approximating
the MAP estimator in the spirit of the SAEM algorithm. We prove its convergence
to a critical point of the observed likelihood with an illustration on images
of handwritten digits.
