expectation

This function computes expectations of 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 

expectation 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 expectations are computed. Its columns
must explicitly be named after the explanatory variables. If <code>NULL</code> (the
default), the joint expectation is computed in a one-row matrix.</dd></dl>

Arguments

Compute expectations of a Gaussian mixture model — expectation

<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 expectations are computed. Its columns
must explicitly be named after the explanatory variables. If <code>NULL</code> (the
default), the joint expectation is computed in a one-row matrix.</dd>

</dl>

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expectation: Compute expectations of a Gaussian mixture model

Description

Usage

Value

Arguments

See Also

Examples