  Many applications require that we learn the parameters of a model from data.
EM is a method used to learn the parameters of probabilistic models for which
the data for some of the variables in the models is either missing or hidden.
There are instances in which this method is slow to converge. Therefore,
several accelerations have been proposed to improve the method. None of the
proposed acceleration methods are theoretically dominant and experimental
comparisons are lacking. In this paper, we present the different proposed
accelerations and try to compare them experimentally. From the results of the
experiments, we argue that some acceleration of EM is always possible, but that
which acceleration is superior depends on properties of the problem.
