gmdbn_air

<p>This Gaussian mixture dynamic Bayesian network is learned from the Beijing
air quality dataset, following the example provided in the documentation page
of function <code>struct_learn</code>.</p>

datasets

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 

gmdbn_air function

Format

Gaussian mixture dynamic Bayesian network learned from the Beijing air
quality dataset — gmdbn_air

Gaussian mixture dynamic Bayesian network learned from the Beijing air
quality dataset

gmdbn_air: Gaussian mixture dynamic Bayesian network learned from the Beijing air quality dataset

Description

Usage

Arguments

Format

See Also