Abstract
The EM (Expectation–Maximization) algorithm is a general-purpose algorithm for maximum likelihood estimation in a wide variety of situations best described as incomplete-data problems. On each iteration of the EM algorithm, there are two steps – called the Expectation step or the E-step and the Maximization step or the M-step. Because of this, the algorithm is called the EM algorithm. Often in practice the log likelihood function cannot be maximized analytically. In such cases, it may be possible to compute iteratively the maximum likelihood estimate using a Newton–Raphson maximization procedure or some variant, provided the total number of parameters in the model is not too large. Another possible alternative is to apply the EM algorithm, which is typically simpler to implement.
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