Calculates the posterioris of Bayes theorem
Bayes4Mixtures(Data, Means, SDs, Weights, IsLogDistribution,
PlotIt, CorrectBorders,Color,xlab,lwd)List with
(1:N,1:L) of Posteriors corresponding to Data
(1:N) denominator of Bayes theorem corresponding to Data
vector (1:N) of data points
vector[1:L] of Means of Gaussians (of GMM),L == Number of Gaussians
vector of standard deviations, estimated Gaussian Kernels, has to be the same length as Means
vector of relative number of points in Gaussians (prior probabilities), has to be the same length as Means
Optional, ==1 if distribution(i) is a LogNormal, default vector of zeros of length L
Optional, Default: FALSE; TRUE do a Plot
Optional, ==TRUE data at right borders of GMM distribution will be assigned to last gaussian, left border vice versa. (default ==FALSE) normal Bayes Theorem
Optional, character vector of colors, default rainbow()
Optional, label of x-axis, default 'Data', see intern R documentation
Width of Line, see intern R documentation
Catharina Lippmann, Onno Hansen-Goos, Michael Thrun
See conference presentation for further explanation.
Thrun M.C.,Ultsch, A.: Models of Income Distributions for Knowledge Discovery, European Conference on Data Analysis, DOI 10.13140/RG.2.1.4463.0244, Colchester 2015.
BayesDecisionBoundaries,AdaptGauss