% 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
%
標簽:
multidimensional
estimation
algorithm
Gaussian
上傳時間:
2013-12-03
上傳用戶:我們的船長