Expectation-maximization is a broadly applicable approach to the iterative computation of maximum likelihood estimates. Each iteration of expectation-maximization method consists of two steps: the expectation step and the maximization step. Expectation-maximization method is useful in a variety of problems where the maximum likelihood estimates are very difficult to find. The basic idea of expectation-maximization method is to relate incomplete data problems to complete data problems where estimation by maximum likelihood method is much simpler.