Expectation Maximization Algorithm
Isobel Claire Gormley, Thomas Brendan Murphy · Encyclopedia of Statistics in Quality and Reliability · 2007
Abstract The expectation maximization (EM) algorithm is a powerful tool for finding maximum‐likelihood estimates in problems where the data are incomplete. Missing data may be an intrinsic feature of the problem or may be artificially imputed to ease estimation. The EM algorithm is an iterative algorithm that consists of two steps—an expectation step (E‐step) and a maximization step (M‐step). The output is a sequence of parameter estimates that converge to a maximum‐likelihood estimate. The implementation of the EM algorithm is illustrated using a finite mixture of exponential densities.