  (Neal and Hinton, 1998) recast maximum likelihood estimation of any given
latent variable model as the minimization of a free energy functional $F$, and
the EM algorithm as coordinate descent applied to $F$. Here, we explore
alternative ways to optimize the functional. In particular, we identify various
gradient flows associated with $F$ and show that their limits coincide with
$F$'s stationary points. By discretizing the flows, we obtain practical
particle-based algorithms for maximum likelihood estimation in broad classes of
latent variable models. The novel algorithms scale to high-dimensional settings
and perform well in numerical experiments.
