但固定一个变量后,另外一个可以通过求导得到,因此可以使用坐标上升法,一次固定一个变量,对另外的求极值,最后逐步逼近极值。对应到EM上,E步估计隐含变量,M步估计其他参数,交替将极值推向最大。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...
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...
1 Introduction 2 The General EM AlgorithmMinka, Thomas P
但固定一个变量后,另外一个可以通过求导得到,因此可以使用坐标上升法,一次固定一个变量,对另外的求极值,最后逐步逼近极值。对应到 EM 上,E 步估计隐含变量,M 步估计其他参数,交替将极值推向最大。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...
The EM algorithm 上面看到使用EM来拟合混合高斯问题,但这只是EM的一个特例 这章会推导出EM的一般形式,他可以解决各种含有隐变量的预估问题(estimation problems with latent variables.) Jensen's inequality 先介绍一下Jensen不等式 首先通过下面的图理解一下,当f是凸函数的时候 ...
(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。1.Jensen不
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...
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The EM algorithm can sometimes converge to degenerate solutions in which the covariance matrix of one of the components of the mixture is singular and the log-likelihood is infinite (most likely resulting in a NaN on computers). In our experience, imposing constraints in the M step to avoid ...
The combination of MAP decoding and maximum likelihood channel estimation can be justified using the EM principle. This leads to an iterative detection and channel estimation algorithm based on the Baum-Welch (BW) algorithm, proposed in [23] and modified for reduced complexity in [24] for non-...