exact_inference_backwards

Given a bn.fit object, the size of the net and a dataset,
performs exact smoothing over the initial evidence taken from the dataset.
Take notice that the smoothing is done backwards in time, as opposed to
forecasting.

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_inference_backwards 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. Should be in the oldest time step</dd>
<dt>ini</dt>
<dd>starting point in the dataset to smooth</dd>
<dt>len</dt>
<dd>length of the smoothing</dd>
<dt>prov_ev</dt>
<dd>variables to be provided as evidence in each forecasting step. Should be in the oldest time step</dd></dl>

Arguments

Performs exact inference smoothing with the GDBN over a dataset — exact_inference_backwards

<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. Should be in the oldest time step</dd>


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


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


<dt>prov_ev</dt>
<dd>variables to be provided as evidence in each forecasting step. Should be in the oldest time step</dd>

</dl>

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exact_inference_backwards: Performs exact inference smoothing with the GDBN over a dataset

Description

Usage

Value

Arguments