Expectation-Maximization Algorithm

The Expectation-Maximization (EM) algorithm is a statistical technique used for finding maximum likelihood estimates of parameters in probabilistic models, especially when the data is incomplete or has missing values. It operates in two steps: the Expectation step (E-step), where the algorithm estimates the expected value of the log-likelihood function, and the Maximization step (M-step), where it maximizes this expected log-likelihood to update the parameters. This iterative process continues until convergence is achieved. The EM algorithm is widely used in various applications, including clustering (such as Gaussian Mixture Models), image processing, and in the training of hidden Markov models.