Machine Learning Series No.6 -- EM algorithm EM算法 1.直观理解 通俗理解:https://blog.csdn.net/v_JULY_v/article/details/81708386 通俗的理解看出就是EM算法由于不知道隐变量的分布,先给出参数的随机初始值,然后根据参数,去得到隐变量的分布,然后根据隐变量和观测变量的共同分布基于最大似然去重新估计参数...
Parameter Estimation for Gaussian Mixture and Hidden Markov Models http://imaging.mrc-cbu.cam.ac.uk/methods/BayesianStuff?action=AttachFile&do=get&target=bilmes-em-algorithm.pdf [2] Yida.Xu: Expectation Maximization roboticcam/machine-learning-notes [3] LongMingsheng <deep learning> lecture Mingshe...
An elegant and powerful method for finding maximum likelihood solutions for models with latent variables is called the expectation-maximization algorithm. —— From 《Pattern Recognition and Machine Learning》 § 9.2.2 例子:三硬币模型,3枚硬币分别记为A、B、C,单独抛下正面出现的概率分别是ππ,p,q,...
EM算法流程:1、初始化分布参数2、重复下列两个操作直到收敛: E步骤:估计隐藏变量的概率分布期望函数; M步骤:根据期望函数重新估计分布参数。 M步公式中下界函数的推导过程:EM算法一个常见的例子就是GMM模型,每个 Algorithm之EM:Expectation Maximization简介、代码实现 EM期望极大算法简介EM算法是Dempster,Laind,Rubin ...
1.EM算法的背景介绍 在极大似然估计中，我们就是用求最值的方法，将使得p(x|θ)取得最大值的参数...
Machine Learning --- GMM & QDA\LDA & EM algorithm 一、单高斯模型GSM(多元正态分布MVN) 当特征为2D时: 马氏距离=翻转坐标系下的欧式距离: 高斯分布证明(极大熵): [例]拉格朗日乘子法对q求导: 服从指数分布族: 证毕。 二、高斯混合模型GMM(多个单高斯的线性叠加,可逼近任意分布,每个高斯是一个聚类中心...
EM 算法,指的是最大期望算法(Expectation Maximization Algorithm,期望最大化算法),是一种迭代算法,在统计学中被用于寻找,依赖于不可观察的隐性变量的概率模型中,参数的最大似然估计。基本思想是首先随机取一个值去初始化待估计的参数值,然后不断迭代寻找更优的参数使得其似然函数比原来的似然函数大。
ans=0#colID>=6代表的是连续型变量ifcolID>=6:mean=1std=1if(colID,C)incontinuousPara:curPara=continuousPara[(colID,C)]mean=curPara[0]std=curPara[1]else:#求平均值和方差 curData=X[curJudgeList,colID]mean=curData.mean()std=curData.std()#print(mean,std)#保存元素 ...
E-Step. Estimate the missing variables in the dataset. M-Step. Maximize the parameters of the model in the presence of the data. The EM algorithm can be applied quite widely, although is perhaps most well known in machine learning for use in unsupervised learning problems, such as density ...
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,...