approximate_inference

Given a bn.fit object, the size of the net and a dataset,
performs approximate forecasting with bnlearns cpdist function over the 
initial evidence taken from the dataset.

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

approximate_inference function

<dl><dt>dt</dt>
<dd>data.table object with the TS data</dd>
<dt>fit</dt>
<dd>bn.fit object</dd>
<dt>obj_vars</dt>
<dd>variables to be predicted</dd>
<dt>ini</dt>
<dd>starting point in the dataset to forecast.</dd>
<dt>rep</dt>
<dd>number of repetitions to be performed of the approximate inference</dd>
<dt>len</dt>
<dd>length of the forecast</dd>
<dt>num_p</dt>
<dd>number of particles to be used by bnlearn</dd></dl>

Arguments

Performs approximate inference forecasting with the GDBN over a dataset — approximate_inference

<dl>

<dt>dt</dt>
<dd>data.table object with the TS data</dd>


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


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


<dt>ini</dt>
<dd>starting point in the dataset to forecast.</dd>


<dt>rep</dt>
<dd>number of repetitions to be performed of the approximate inference</dd>


<dt>len</dt>
<dd>length of the forecast</dd>


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

</dl>

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approximate_inference: Performs approximate inference forecasting with the GDBN over a dataset

Description

Usage

Value

Arguments