BayesFor2GMM {AdaptGauss} | R Documentation |
Calculates the posterioris of Bayes theorem, splits the GMM in two groups beforehand.
BayesFor2GMM(Data, Means, SDs, Weights, IsLogDistribution = Means * 0,
Ind1 = c(1:floor(length(Means)/2)), Ind2 = c((floor(length(Means)/2)
+ 1):length(Means)), PlotIt = 0, CorrectBorders = 0)
Data |
vector (1:N) of data points |
Means |
vector[1:L] of Means of Gaussians (of GMM),L == Number of Gaussians |
SDs |
vector of standard deviations, estimated Gaussian Kernels, has to be the same length as Means |
Weights |
vector of relative number of points in Gaussians (prior probabilities), has to be the same length as Means |
IsLogDistribution |
Optional, ==1 if distribution(i) is a LogNormal, default vector of zeros of length L |
Ind1 |
indices from (1:C) such that [M(Ind1),S(Ind1) ,W(Ind1) ]is one mixture, [M(Ind2),S(Ind2) ,W(Ind2) ] the second mixture default Ind1= 1:C/2, Ind2= C/2+1:C |
Ind2 |
indices from (1:C) such that [M(Ind1),S(Ind1) ,W(Ind1) ]is one mixture, [M(Ind2),S(Ind2) ,W(Ind2) ] the second mixture default Ind1= 1:C/2, Ind2= C/2+1:C |
PlotIt |
Optional, Default: FALSE; TRUE do a Plot |
CorrectBorders |
Optional, ==TRUE data at right borders of GMM distribution will be assigned to last gaussian, left border vice versa. (default ==FALSE) normal Bayes Theorem |
See conference presentation for further explanation.
List With
(1:N,1:L) of Posteriors corresponding to Data
(1:N) denominator of Bayes theorem corresponding to Data
Alfred Ultsch, Michael Thrun
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.
See Also
BayesDecisionBoundaries,AdaptGauss