AdaptGauss (version 1.1.0)

BayesForMixes: Posterioris of Bayes Theorem

Description

Calculates the posterioris of Bayes theorem

Usage

BayesForMixes(Data, Means, SDs, Weights, IsLogDistribution, PlotIt, CorrectBorders)

Arguments

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

Value

• List with
• Posteriors
  (1:N,1:L) of Posteriors corresponding to Data
• NormalizationFactor
  (1:N) denominator of Bayes theorem corresponding to Data

Details

See conference presentation for further explanation.

References

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