exact_prediction_step

Given a bn.fit object and some variables, performs
exact MVN 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

exact_prediction_step function

<dl><dt>fit</dt>
<dd>list with the mu and sigma of the MVN model</dd>
<dt>variables</dt>
<dd>variables to be predicted</dd>
<dt>evidence</dt>
<dd>a list with the provided evidence</dd></dl>

Arguments

Performs exact inference in a time slice of the dbn — exact_prediction_step

<dl>

<dt>fit</dt>
<dd>list with the mu and sigma of the MVN model</dd>


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


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

</dl>

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exact_prediction_step: Performs exact inference in a time slice of the dbn

Description

Usage

Value

Arguments