klfor2normals

the location parameter of the first distribution

theta1.mu

the covariance matrix of the first distribution

theta1.sigma

the location parameter of the second distribution

theta2.mu

the covariance matrix of the second distribution

theta2.sigma

klfor2normals
Compute the Kullback-Leibler divergence for 2 normal multivariate distributions

A robust clustering algorithm (Model-Based) similar to Expectation
Maximization for finite mixture normal distributions is implemented,
its main advantage is that the estimator is resistant to outliers,
that means that results of parameter estimation are still correct when there
are atypical values in the sample
(see Gonzalez, Maronna, Yohai and Zamar (2021)
<arxiv:2102.06851>).

klfor2normals: klfor2normals Compute the Kullback-Leibler divergence for 2 normal multivariate distributions

Description

Usage

Arguments

Value