Bayes4Mixtures {AdaptGauss} | R Documentation |
Calculates the posterioris of Bayes theorem
Bayes4Mixtures(Data, Means, SDs, Weights, IsLogDistribution,
PlotIt, CorrectBorders,Color,xlab,lwd)
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 |
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 |
Color |
Optional, character vector of colors, default rainbow() |
xlab |
Optional, label of x-axis, default 'Data', see intern R documentation |
lwd |
Width of Line, see intern R documentation |
See conference presentation for further explanation.
List with
Posteriors |
(1:N,1:L) of Posteriors corresponding to Data |
NormalizationFactor |
(1:N) denominator of Bayes theorem corresponding to Data |
Catharina Lippmann, Onno Hansen-Goos, 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.
BayesDecisionBoundaries
,AdaptGauss