％ 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 likelihood difference between 2 iterations ([] for none) ％ maxiter - maximum number of iteration allowed ([] for none) ％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) ％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) ％ ％ Ouputs: ％ W(1,k) - estimated weights of GM ％ M(d,k) - estimated mean vectors of GM ％ V(d,d,k) - estimated Covariance matrices of GM ％ L - log likelihood of estimates ％
标签： multidimensional estimation algorithm Gaussian
上传时间： 2013-12-03
上传用户：我们的船长
The angles in degrees of the two spatially propagating signals Compute the array response vectors of the two signals Compute the true Covariance matrix
标签： propagating the spatially response
上传时间： 2014-01-24
上传用户：1966640071
使用INTEL矢量统计类库的程序,包括以下功能: Raw and central moments up to 4th order Kurtosis and Skewness Variation Coefficient Quantiles and Order Statistics Minimum and Maximum Variance-Covariance/Correlation matrix Pooled/Group Variance-Covariance/Correlation Matrix and Mean Partial Variance-Covariance/Correlation matrix Robust Estimators for Variance-Covariance Matrix and Mean in presence of outliers
标签： 61623 and Kurtosis central
上传时间： 2017-05-14
上传用户：yzy6007
Many good textbooks exist on probability and random processes written at the under- graduate level to the research level. However, there is no one handy and ready book that explains most of the essential topics, such as random variables and most of their frequently used discrete and continuous probability distribution functions; moments, transformation, and convergences of random variables; characteristic and generating functions; estimation theory and the associated orthogonality principle; vector random variables; random processes and their autoCovariance and cross-Covariance functions; sta- tionarity concepts; and random processes through linear systems and the associated Wiener and Kalman filters.
标签： Probability Processes Random and
上传时间： 2020-05-31
上传用户：shancjb
