Expectation Maximization

Iterative method to find the maximum likelihood or maximum a posteriori (MAP) estimates of the parameters of a statistical model.

The EM algorithm alternates between performing an expectation (E) step, which provides the expectation of the conditional log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step.