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.