initialize_cl_cpp

Create a causality list and initialize it

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

initialize_cl_cpp function

<dl><dt>ordering</dt>
<dd>a list with the order of the variables in t_0</dd>
<dt>size</dt>
<dd>the size of the DBN</dd></dl>

Arguments

Create a causality list and initialize it — initialize_cl_cpp

<dl>

<dt>ordering</dt>
<dd>a list with the order of the variables in t_0</dd>


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

</dl>

initialize_cl_cpp: Create a causality list and initialize it

Description

Usage

Value

Arguments