Research and Application of EM Algorithm

Following the description of traditional maximum likelihood estimation methods and the discussions on their disadvantages.EM algorithm is an iterative algorithm,every iteration to ensure that the likelihood function can be increased,and the convergence to a local maxima.Presents an EM algorithm that can be used to deal with missing data problems,where the details of the EM algorithm and its realization procedure have been analyzed.Algorithm named because each iterative algorithm includes two steps: the first step in seeking expectations(Expectation Step),known as the E step;the second step for maxima(Maximization Step),known as step-by-step M.EM algorithm used to calculate the principal based on incomplete data,maximum likelihood estimation.This is then followed by applying the proposed EM algorithm to the parameter estimation of state space models.The paper also presents the Kalman smoothing based parameter estimation methods for linear state space models.