CDFMixtures

at these locations N(Means,Sdevs)*Weights is used for cdf calcuation, NOTE: Kernels are usually (but not necessarily) sorted and unique

Kernels

vector(1:L), Means of Gaussians,  L ==  Number of Gaussians

Means

estimated Gaussian Kernels = standard deviations

optional, relative number of points in Gaussians (prior probabilities): 
                                  sum(Weights) ==1, default weight is 1/L

Weights

Optional, if IsLogDistribution(i)==1, then mixture is lognormal
                                  default == 0*(1:L)

IsLogDistribution

returns the cdf (cumulative distribution function) of a mixture model of gaussian or log gaussians

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.

CDFMixtures: cumulative distribution of mixture model

Description

Usage

Arguments

Value

See Also