A method for producing a sequence of parameter estimates that, under mild regularity conditions, converges to the maximum likelihood estimator. Of particular importance in the context of incomplete data problems. The algorithm consists of two steps, known as the E, or Expectation step and the M, or Maximization step. In the former, the expected value of the log likelihood conditional on the observed data and the current estimates of the parameters, is found. In the M step, this function is maximized to give updated parameter estimates that increase the likelihood. The two steps are alternated until convergence is achieved. The algorithm may, in some cases, be very slow to converge.