add_nodes

This function adds nodes to a Gaussian mixture graphical model. If this model
is a dynamic Bayesian network, the nodes are added to each of its transition
models. For each added node, a one-component univariate Gaussian mixture
model is created with mean 0 and variance 1.

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 

add_nodes function

<dl><dt>gmgm</dt>
<dd>An object of class <code>gmbn</code> or <code>gmdbn</code>. If <code>NULL</code>, a
<code>gmbn</code> object is created with the added nodes.</dd>
<dt>nodes</dt>
<dd>A character vector containing the added nodes.</dd></dl>

Arguments

Add nodes to a Gaussian mixture graphical model — add_nodes

<dl>

<dt>gmgm</dt>
<dd>An object of class <code>gmbn</code> or <code>gmdbn</code>. If <code>NULL</code>, a
<code>gmbn</code> object is created with the added nodes.</dd>


<dt>nodes</dt>
<dd>A character vector containing the added nodes.</dd>

</dl>

add_nodes: Add nodes to a Gaussian mixture graphical model

Description

Usage

Value

Arguments

See Also

Examples