In EM, one deals with problems that have hidden variables as well as model parameters.

The entropy of a probability distribution is always larger equal 0 and smaller equal 1.

In the M-step, the hidden variables are updated.

The EM algorithm can be applied to hidden Markov models and mixture models.

The E-step increases the lower bound on the log-likelihood.

In the E-step, the model parameters are updated.

Maximum Entropy requires minimal prior assumptions to assign a-priori-probabilities.

The E-step increases the upper bound on the log-likelihood.