In statistics, an expectation–maximization ( EM ) algorithm is an
iterative method to find (local) 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.
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