The stochastic approximation version of EM (SAEM) proposed by Delyon et al. (1999) is a powerful alternative to EM when the E-step is intractable. Convergence of SAEM toward a maximum of the observed likelihood is established when the unobserved data are simulated at each iteration under the conditional distribution. We show that this very restrictive assumption can be weakened. Indeed, the results of Benveniste et al. for stochastic approximation with markovian perturbations are used to establish...
The estimation of probabilistic deformable template models in
computer vision or of probabilistic atlases in Computational Anatomy
are core issues in both fields.
A first coherent statistical framework where the geometrical variability is
modelled as a hidden
random variable has been
given by [S. Allassonnière ,
(2007) 3–29]. They introduce
a Bayesian approach and
mixture of them to estimate deformable template models.
A consistent stochastic algorithm has been introduced in [S. Allassonnière ...
The stochastic approximation version of EM (SAEM) proposed by Delyon (1999) is
a powerful alternative to EM when the E-step is intractable. Convergence of
SAEM toward a maximum of the observed likelihood is established when
the unobserved data are simulated at each iteration under the conditional
distribution. We show that this very restrictive assumption can be weakened. Indeed,
the results of Benveniste for stochastic approximation
with Markovian perturbations are used to establish the convergence
of...
Download Results (CSV)