particles

This function initializes particles to perform (approximate) inference in a
Gaussian mixture graphical model. Particles consist in weighted sample
sequences propagated forward in time by sampling the model and aggregated to
obtain the inferred values (Koller and Friedman, 2009).

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 

particles function

<dl><dt>seq</dt>
<dd>A data frame containing the observation sequences for which
particles are initialized. If <code>NULL</code> (the default), the initialization
is performed for a single sequence.</dd>
<dt>col_weight</dt>
<dd>A character string corresponding to the column name of the
resulting data frame that describes the particle weight.</dd>
<dt>n_part</dt>
<dd>A positive integer corresponding to the number of particles
initialized for each observation sequence.</dd></dl>

Arguments

Initialize particles to perform inference in a Gaussian mixture graphical
model — particles

<dl>

<dt>seq</dt>
<dd>A data frame containing the observation sequences for which
particles are initialized. If <code>NULL</code> (the default), the initialization
is performed for a single sequence.</dd>


<dt>col_weight</dt>
<dd>A character string corresponding to the column name of the
resulting data frame that describes the particle weight.</dd>


<dt>n_part</dt>
<dd>A positive integer corresponding to the number of particles
initialized for each observation sequence.</dd>

</dl>

particles: Initialize particles to perform inference in a Gaussian mixture graphical model

Description

Usage

Value

Arguments

References

See Also

Examples