approx_prediction_step

Given a bn.fit object and some variables, performs
particle inference over such variables in the net for a given time slice.

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

approx_prediction_step function

<dl><dt>fit</dt>
<dd>bn.fit object</dd>
<dt>variables</dt>
<dd>variables to be predicted</dd>
<dt>particles</dt>
<dd>a list with the provided evidence</dd>
<dt>n</dt>
<dd>the number of particles to be used by bnlearn</dd></dl>

Arguments

Performs approximate inference in a time slice of the dbn — approx_prediction_step

<dl>

<dt>fit</dt>
<dd>bn.fit object</dd>


<dt>variables</dt>
<dd>variables to be predicted</dd>


<dt>particles</dt>
<dd>a list with the provided evidence</dd>


<dt>n</dt>
<dd>the number of particles to be used by bnlearn</dd>

</dl>

approx_prediction_step: Performs approximate inference in a time slice of the dbn

Description

Usage

Value

Arguments