Expectation-Maximization

Expectation-Maximization (EM) is a statistical technique used for finding maximum likelihood estimates of parameters in probabilistic models, particularly when the data is incomplete or has missing values. The method involves two main steps: the Expectation step, where the expected value of the log-likelihood is computed using the current parameter estimates, and the Maximization step, where parameters are updated to maximize this expected log-likelihood. EM is commonly used in various fields, including machine learning, computer vision, and bioinformatics, to handle data with latent variables. Its iterative nature allows it to converge to a local maximum of the likelihood function, making it a powerful tool for model fitting.