structure

This function provides the graphical structure of a Gaussian mixture
graphical model.

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 

structure function

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

Arguments

Provide the graphical structure of a Gaussian mixture graphical model — structure

<dl>

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

</dl>

structure: Provide the graphical structure of a Gaussian mixture graphical model

Description

Usage

Value

Arguments

Examples