ExpectationMaximization algorithm to calculate optimal Gaussian Mixture Model for given data in one Dimension.
1  EMGauss(Data, Means, SDs,Weights, MaxNumberofIterations,fast)

Data 
vector of data points 
Means 
vector(1:L), Means of Gaussians, L == Number of Gaussians 
SDs 
estimated Gaussian Kernels = standard deviations 
Weights 
optional, relative number of points in Gaussians (prior probabilities): sum(Weights) ==1, default weight is 1/L 
MaxNumberofIterations 
Optional, Number of Iterations; default=10 
fast 
Default: FALSE: Using mclust's EM see function 
No adding or removing of Gaussian kernels. Number of Gaussian hast to be set by the length of the vector of Means, SDs and Weights.
This EM is only for univariate data. For multivariate data see package mclust
List with
Means 
means of GMM generated by EM algorithm 
SDs 
standard deviations of GMM generated by EM algorithm 
Weights 
prior probabilities of Gaussians 
Onno HansenGoos, Michael Thrun
Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006, p 435 ff
AdaptGauss
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