AIC.dbn

Generic method for calculating the Akaike information criterion (AIC) of a 
"dbn" S3 object given some data. Calls bnlearn's <code><a href="/link/AIC?package=dbnR&version=0.7.8" data-mini-rdoc="dbnR::AIC">AIC</a></code> underneath.

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

AIC.dbn function

<dl><dt>object</dt>
<dd>the structure of the network</dd>
<dt>...</dt>
<dd>additional parameters for the network scoring</dd>
<dt>k</dt>
<dd>the penalty parameter</dd></dl>

Arguments

Generic method for calculating the Akaike information criterion (AIC) of a 
"dbn" S3 object given some data. Calls bnlearn's <code><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></code> underneath.

Calculate the AIC of a dynamic Bayesian network — AIC.dbn

<dl>

<dt>object</dt>
<dd>the structure of the network</dd>


<dt>...</dt>
<dd>additional parameters for the network scoring</dd>


<dt>k</dt>
<dd>the penalty parameter</dd>

</dl>

AIC.dbn: Calculate the AIC of a dynamic Bayesian network

Description

Usage

Value

Arguments