learn_dbn_struc

Learns a gaussian dynamic Bayesian network from a dataset. 
It allows the creation of markovian n nets rather than only markov 1.

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

learn_dbn_struc function

<dl><dt>dt</dt>
<dd>the data.frame or data.table to be used</dd>
<dt>size</dt>
<dd>number of time slices of the net. Markovian 1 would be size 2</dd>
<dt>method</dt>
<dd>the structure learning method of choice to use</dd>
<dt>f_dt</dt>
<dd>previously folded dataset, in case some specific rows have to be removed after the folding</dd>
<dt>...</dt>
<dd>additional parameters for <code>rsmax2</code> function</dd></dl>

Arguments

Learns the structure of a markovian n DBN model from data — learn_dbn_struc

<dl>

<dt>dt</dt>
<dd>the data.frame or data.table to be used</dd>


<dt>size</dt>
<dd>number of time slices of the net. Markovian 1 would be size 2</dd>


<dt>method</dt>
<dd>the structure learning method of choice to use</dd>


<dt>f_dt</dt>
<dd>previously folded dataset, in case some specific rows have to be removed after the folding</dd>


<dt>...</dt>
<dd>additional parameters for <code>rsmax2</code> function</dd>

</dl>

learn_dbn_struc: Learns the structure of a markovian n DBN model from data

Description

Usage

Value

Arguments

Examples