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The Expectation-Maximization (EM) algorithms are iterative methods that find the maximum likelihood estimated parameters over a statistical model. Each algorithm iteration is performed in two steps - The Expectation step (E-step) which measures the log-likelihood expectation on some already estimated parameter. Then a Maximization step (M-step) maximizes the expected log-likelihood calculated in the E-step.