sampling

This function samples a Gaussian mixture 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 

sampling function

<dl><dt>gmm</dt>
<dd>An object of class <code>gmm</code>.</dd>
<dt>data_x</dt>
<dd>A data frame or numeric matrix containing observations of the
explanatory variables if conditional sampling is performed. Its columns must
explicitly be named after the explanatory variables. If <code>NULL</code> (the
default), joint sampling is performed.</dd>
<dt>n</dt>
<dd>A non-negative integer corresponding to the number of samples. If
conditional sampling is performed, this argument is ignored.</dd></dl>

Arguments

Sample a Gaussian mixture model — sampling

<dl>

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


<dt>data_x</dt>
<dd>A data frame or numeric matrix containing observations of the
explanatory variables if conditional sampling is performed. Its columns must
explicitly be named after the explanatory variables. If <code>NULL</code> (the
default), joint sampling is performed.</dd>


<dt>n</dt>
<dd>A non-negative integer corresponding to the number of samples. If
conditional sampling is performed, this argument is ignored.</dd>

</dl>

sampling: Sample a Gaussian mixture model

Description

Usage

Value

Arguments

See Also

Examples