Expectation-Maximization Algorithm

An iterative procedure for maximum-likelihood (or maximum a posteriori) parameter estimation in probabilistic models with latent (unobserved) variables that alternates between computing expected sufficient statistics given current parameters (E-step) and optimizing parameters to maximize the expected complete-data log-likelihood (M-step).