L(\theta|Y_{obs},Z)\propto\theta_1^{z_1+z_2}\theta_2^{z_3+z_4}\theta_3^{y_5} EM迭代公式: \hat{\theta}_1=\frac{z_1+z_2}{\sum_{i=1}^4z_i+y_5},\hat{\theta}_2=\frac{z_3+z_4}{\sum_{i=1}^4z_i+y_5},\hat{\theta}_3=\frac{y_5}{\sum_{i=1}^4...
EM中还有“硬”指定和“软”指定的概念,“软”指定看似更为合理,但计算量要大,“硬”指定在某些场合如K-means中更为实用(要是保持一个样本点到其他所有中心的概率,就会很麻烦)。 另外,EM的收敛性证明方法确实很牛,能够利用log的凹函数性质,还能够想到利用创造下界,拉平函数下界,优化下界的方法来逐步逼近极大值...
The Expectation-Maximization (EM) algorithm is a broadly applicable approach to the iterative computation of maximum likelihood estimates in a wide variety of incomplete-data problems. The EM algorithm has a number of desirable properties, such as its numerical stability, reliable global convergence, ...
(EM算法)The EM Algorithm http://www.cnblogs.com/jerrylead/archive/2011/04/06/2006936.html http://blog.sina.com.cn/s/blog_a7da5cda010158b3.htmlEM算法 一个简单的例子 EM算法有它的缺陷:“坏”的参数初始值设置可以导致EM算法陷进一些局最优点;EM算法的收敛速度比较慢;只有在不存在直接解决的算法...
E 步很简单,按照一般 EM 公式得到: 简单解释就是每个样例 i 的隐含类别z(i)为 j 的概率可以通过后验概率计算得到。 在M 步中,我们需要在固定 i( (i))后最大化最大似然估计,也就是 这是将 (i)的 k 种情况展开后的样子,未知参数∅j, μj和Σj。
(EM算法)The EM Algorithm EM是我一直想深入学习的算法之一,第一次听说是在NLP课中的HMM那一节,为了解决HMM的参数估计问题,使用了EM算法。在之后的MT中的词对齐中也用到了。在Mitchell的书中也提到EM可以用于贝叶斯网络中。 下面主要介绍EM的整个推导过程。
This book presents an organized and well-knit account of the theory, methodology, extensions, and major applications of the Expectation-Maximization (EM) algorithm. It includes applications in the standard statistical contexts such as regression, factor analysis, variance components estimation, repeated-...
The EM algorithm Since we are able to write the Gaussian mixture model as a latent-variable model, we can use theEM algorithmto find the maximum likelihood estimators of its parameters. Starting from an initial guess of the parameter vector ...
The EM Algorithm and ExtensionsG MACLACHLANT KRISHNANdate-added={2012-08-30 16:20:27 +0200}, date-modified={2012-08-30 16:20:27 +0200}, project={fremdliteratur}doi:10.1080/00401706.1998.10485534Geoffrey J. McLachlanThriyambakam Krishnan...
The EM Algorithm and Extensions remains the only single source to offer a complete and unified treatment of the theory, methodology, and applications of the EM algorithm. The highly applied area of statistics here outlined involves applications in regression, medical imaging, finite mixture analysis,...