add_attr_to_fit

Adds the mu vector and sigma matrix as attributes to the bn.fit or dbn.fit 
object to allow performing exact MVN inference on both cases. It also adds
the number of time slices of the net for future inference.

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

add_attr_to_fit function

<dl><dt>fit</dt>
<dd>a fitted bn or dbn</dd>
<dt>size</dt>
<dd>number of time slices of the net</dd></dl>

Arguments

Adds the mu vector and sigma matrix as attributes to the bn.fit or dbn.fit object — add_attr_to_fit

<dl>

<dt>fit</dt>
<dd>a fitted bn or dbn</dd>


<dt>size</dt>
<dd>number of time slices of the net</dd>

</dl>

add_attr_to_fit: Adds the mu vector and sigma matrix as attributes to the bn.fit or dbn.fit object

Description

Usage

Value

Arguments