logLik.dbn.fit

Generic method for calculating the log-likelihood of a 
"dbn.fit" S3 object given some data. Calls bnlearn's <code><a href="/link/logLik?package=dbnR&version=0.7.8" data-mini-rdoc="dbnR::logLik">logLik</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

logLik.dbn.fit function

<dl><dt>object</dt>
<dd>the fitted network</dd>
<dt>dt</dt>
<dd>the dataset to calculate the score of the network</dd>
<dt>...</dt>
<dd>additional parameters for the network scoring</dd></dl>

Arguments

Generic method for calculating the log-likelihood of a 
"dbn.fit" S3 object given some data. Calls bnlearn's <code><a href='https://rdrr.io/r/stats/logLik.html'>logLik</a></code> underneath.

Calculate the log-likelihood of a dynamic Bayesian network — logLik.dbn.fit

<dl>

<dt>object</dt>
<dd>the fitted network</dd>


<dt>dt</dt>
<dd>the dataset to calculate the score of the network</dd>


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

</dl>

logLik.dbn.fit: Calculate the log-likelihood of a dynamic Bayesian network

Description

Usage

Value

Arguments