  The EM algorithm is one of the most popular algorithm for inference in latent
data models. The original formulation of the EM algorithm does not scale to
large data set, because the whole data set is required at each iteration of the
algorithm. To alleviate this problem, Neal and Hinton have proposed an
incremental version of the EM (iEM) in which at each iteration the conditional
expectation of the latent data (E-step) is updated only for a mini-batch of
observations. Another approach has been proposed by Capp\'e and Moulines in
which the E-step is replaced by a stochastic approximation step, closely
related to stochastic gradient. In this paper, we analyze incremental and
stochastic version of the EM algorithm as well as the variance reduced-version
of Chen et. al. in a common unifying framework. We also introduce a new version
incremental version, inspired by the SAGA algorithm by Defazio et. al. We
establish non-asymptotic convergence bounds for global convergence. Numerical
applications are presented in this article to illustrate our findings.
