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Expectation-maximization (EM) is an iterative method used to find maximum likelihood estimates of parameters in probabilistic models, where the model depends on unobserved, also called latent, variables. EM alternates between performing an expectation (E) step, which computes an expectation of the likelihood by including the latent variables as if they were observed, and a maximization (M) step, which computes the maximum likelihood estimates of the parameters by maximizing the expected likelihood found in the E step. The parameters found on the M step are then used to start another E step, and the process is repeated until some criterion is satisfied. EM is frequently used for data clustering like for example in Gaussian mixtures or in the Baum-Welch training of a Hidden Markov Model.
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