Expectation-Maximization Framework

An iterative optimization framework for parameter estimation in latent variable models that alternates between an E-step (computing the posterior distribution) and an M-step (maximizing the complete-data log-likelihood), guaranteeing a monotonically non-decreasing likelihood but possibly converging to a local optimum.