Expectation–maximization Algorithm
In statistics, an expectation–maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.
Read more about Expectation–maximization Algorithm: History, Introduction, Description, Properties, Proof of Correctness, Alternative Description, Applications, Filtering and Smoothing EM Algorithms, Variants, Relation To Variational Bayes Methods, α-EM Algorithm, Geometric Interpretation, Further Reading