RandomLogGMM

vector[1:L] of Means of Gaussians (of GMM)

Means

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

Weights

Optional, ==1 if distribution(i) is a LogNormal, default vector of Zeros of Length L

IsLogDistribution

Optional, number of point for log or GMM generated

TotalNoPoints

Function finds the intersections of Gaussians or LogNormals

log GMM

Multimodal distributions can be modelled as a mixture of components. The model is derived using the Pareto Density Estimation (PDE) for an estimation of the pdf. PDE has been designed in particular to identify groups/classes in a dataset. Precise limits for the classes can be calculated using the theorem of Bayes. Verification of the model is possible by QQ plot, Chi-squared test and Kolmogorov-Smirnov test.

RandomLogGMM: Random Number Generator for Log or Gaussian Mixture Model

Description

Usage

Arguments

Value

See Also