split_comp

This function splits a mixture component of a Gaussian mixture model using
the singular value decomposition of the covariance matrix (Zhang
et al., 2003).

Gaussian mixture graphical models include Bayesian networks and
dynamic Bayesian networks (their temporal extension) whose local probability
distributions are described by Gaussian mixture models. They are powerful
tools for graphically and quantitatively representing nonlinear dependencies
between continuous variables. This package provides a complete framework to
create, manipulate, learn the structure and the parameters, and perform
inference in these models. Most of the algorithms are described in the PhD
thesis of Roos (2018) <https://tel.archives-ouvertes.fr/tel-01943718>.

J<c3><a9>r<c3><a9>my Roos

gmgm

Gaussian Mixture Graphical Model Learning and Inference

Jérémy Roos

RATP Group 

split_comp function

<dl><dt>gmm</dt>
<dd>An object of class <code>gmm</code>.</dd>
<dt>comp</dt>
<dd>An integer corresponding to the index of the split mixture
component.</dd>
<dt>n_sub</dt>
<dd>A positive integer corresponding to the number of subcomponents.</dd>
<dt>space</dt>
<dd>A numeric value in [0, 1[ corresponding to the space between the
subcomponents.</dd></dl>

Arguments

Split a mixture component of a Gaussian mixture model — split_comp

<dl>

<dt>gmm</dt>
<dd>An object of class <code>gmm</code>.</dd>


<dt>comp</dt>
<dd>An integer corresponding to the index of the split mixture
component.</dd>


<dt>n_sub</dt>
<dd>A positive integer corresponding to the number of subcomponents.</dd>


<dt>space</dt>
<dd>A numeric value in [0, 1[ corresponding to the space between the
subcomponents.</dd>

</dl>

split_comp: Split a mixture component of a Gaussian mixture model

Description

Usage

Value

Arguments

References

See Also

Examples