kldiv: Kullback-Leibler Divergence of _i_th Student's t Mixture...

In merkys/MixtureFitting: Fitting of univariate mixture distributions to data using various approaches

Kullback–Leibler Divergence of ith Student's t Mixture component.

Description

Measures Kullback–Leibler divergence of ith Student's t Mixture component using Dirac's delta function. Implemented according to Chen et al. (2004).

Usage

    kldiv( x, p, k )

Arguments

x	data vector
p	vector of Student's t mixture parameters. Structure of p vector is p = c( A1, A2, ..., An, mu1, mu2, ..., mun, k1, k2, ..., kn, ni1, ni2, ..., nin ), where n is number of mixture components, Ai is the proportion of i-th component, mui is the center of i-th component, ki is the concentration of i-th component and nii is the degrees of freedom of i-th component.
k	number of the component.

Value

Kullback–Leibler divergence as double.

Author(s)

Andrius Merkys

References

Chen, S.; Wang, H. & Luo, B. Greedy EM Algorithm for Robust T-Mixture Modeling Third International Conference on Image and Graphics (ICIG'04), Institute of Electrical & Electronics Engineers (IEEE), 2004, 548–551

merkys/MixtureFitting documentation built on May 25, 2024, 9:01 a.m.