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\name{kldiv}
\alias{kldiv}
\docType{methods}
\title{Kullback--Leibler Divergence of \emph{i}th Student's t Mixture component.}
\description{
Measures Kullback--Leibler divergence of \emph{i}th Student's t
Mixture component using Dirac's delta function. Implemented
according to Chen et al. (2004).
}
\usage{
kldiv( x, p, k )
}
\arguments{
\item{x}{data vector}
\item{p}{
vector of Student's t mixture parameters. Structure of p vector is
p = c( A1, A2, ..., A\emph{n}, mu1, mu2, ..., mu\emph{n}, k1, k2, ..., k\emph{n}, ni1, ni2, ..., ni\emph{n} ),
where \emph{n} is number of mixture components,
A\emph{i} is the proportion of \emph{i}-th component,
mu\emph{i} is the center of \emph{i}-th component,
k\emph{i} is the concentration of \emph{i}-th component and
ni\emph{i} is the degrees of freedom of \emph{i}-th component.
}
\item{k}{
number of the component.
}
}
\value{
Kullback--Leibler divergence as double.
}
\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
}
\author{Andrius Merkys}
|
