A Gentle Tutorial of the EM Algorithm and its Applicationto Parameter Estimation for GaussianMixture and Hidden Markov Models
教程：EM算法
EM算法即期望最大化算法，在统计自然语言处理中应用非常多，在这里向大家推荐一篇关于EM的教程，希望大家对EM能有所了解。下面是教程的摘要。
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.