Expectation Maximization Algorithm

The expectation-maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori estimates of parameters in statistical models whose likelihood depends on unobserved latent variables. It alternates between an expectation step, which computes the expected value of the log-likelihood using the current parameter estimate, and a maximization step, which computes new parameters maximizing that expectation. Arthur Dempster, Nan Laird and Donald Rubin formalized and named the algorithm in a widely cited 1977 paper, though earlier researchers had proposed versions of the method in specific contexts before that. This description is adapted from Wikipedia contributors under CC BY-SA 4.0; changes were made. https://creativecommons.org/licenses/by-sa/4.0/