network

This function displays the graphical structure of a Gaussian mixture
Bayesian network.

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 

network function

<dl><dt>gmbn</dt>
<dd>An object of class <code>gmbn</code>.</dd></dl>

Arguments

Display the graphical structure of a Gaussian mixture Bayesian network — network

<dl>

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

</dl>

network: Display the graphical structure of a Gaussian mixture Bayesian network

Description

Usage

Value

Arguments

Examples