％ EM algorithm for k multidimensional Gaussian mixture estimation ％ ％ Inputs: ％ X(n,d) - input data, n=number of observations, d=dimension of variable ％ k - maximum number of Gaussian components allowed ％ ltol - percentage of the log likeli

fit_mix_gaussian - fit parameters for a mixed-gaussian distribution using EM algorithm format: [u,sig,t,iter] = fit_mix_gaussian( X,M ) input: X - input samples, Nx1 vector M - number of gaussians which are assumed to compose the distributi

：探索一种能有效抑制和消除颅脑MRI运动伪影产生的方法，以提高图像质量和工作效率。方法：利用包装CT管球的海绵，设计并制作了2 cm x 7．5 cm x 22 cm，3 cm x 7．5 cm x 22 cm，4 cm×7．5 cm x 22 em等3种规格的海绵垫， 配合使用颅脑正交线圈进行颅脑MRI检查。结果：采用“加垫法”行颅脑MRI检查2 638例。其中，一次性扫描成功2 633 例(占99．81％)，失败5例(占0．19％)； 在扫描失败的5例患者中，第2次扫描时配合使用

fit_mix_2D_gaussian - fit parameters for a 2D mixed-gaussian distribution using EM algorithm format: [u,covar,t,iter] = fit_mix_2D_gaussian( X,M ) input: X - input samples, Nx2 vector M - number of gaussians which are assumed to compose the

不错的GM_EM代码。用于聚类分析等方面。- GM_EM- fit a Gaussian mixture model to N points located in n-dimensional space. Note: This function requires the Statistical Toolbox and, if you wish to plot (for k = 2), the function error_ellipse Elem