init_list_cpp

internal

Learning and inference over dynamic Bayesian networks of arbitrary
Markovian order. Extends some of the functionality offered by the 'bnlearn'
package to learn the networks from data and perform exact inference.
It offers three structure learning algorithms for dynamic Bayesian networks:
Trabelsi G. (2013) <doi:10.1007/978-3-642-41398-8_34>, Santos F.P. and Maciel C.D. (2014)
<doi:10.1109/BRC.2014.6880957>, Quesada D., Bielza C. and Larrañaga P. (2021)
<doi:10.1007/978-3-030-86271-8_14>. It also offers the possibility to perform
forecasts of arbitrary length. A tool for visualizing the structure of the
net is also provided via the 'visNetwork' package.

David Quesada

dbnR

Dynamic Bayesian Network Learning and Inference

Gabriel Valverde

init_list_cpp function

<dl><dt>nodes</dt>
<dd>the names of the nodes</dd>
<dt>size</dt>
<dd>the size of the DBN</dd>
<dt>n_inds</dt>
<dd>the number of particles</dd></dl>

Arguments

Initialize the particles — init_list_cpp

<dl>

<dt>nodes</dt>
<dd>the names of the nodes</dd>


<dt>size</dt>
<dd>the size of the DBN</dd>


<dt>n_inds</dt>
<dd>the number of particles</dd>

</dl>

init_list_cpp: Initialize the particles

Description

Usage

Value

Arguments