文件名称：A Gentle Tutorial of the EM Algorithm and its apllication to parameter estimation for Gaussian Mixture and Hidden Markov Models
文件大小：166KB
文件格式：PDF
更新时间：2012-08-02 06:48:22
EM Algorithm
We describe the maximum-likelihood parameter estimation problem and how the Expectation- Maximization (EM) algorithm can be used for its solution. We first describe the abstract form of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2) finding the parameters of a hidden Markov model (HMM) (i.e., the Baum-Welch algorithm) for both discrete and Gaussian mixture observation models. We derive the update equations in fairly explicit detail but we do not prove any convergence properties. We try to emphasize intuition rather than mathematical rigor.