predict.motbf_fit

Predict from an MoTBF Bayesian Network

Learning, manipulation and evaluation of mixtures of truncated basis functions
(MoTBFs), which include mixtures of polynomials (MOPs) and mixtures of truncated
exponentials (MTEs). MoTBFs are a flexible framework for modelling hybrid Bayesian
networks (I. Pérez-Bernabé, A. Salmerón, H. Langseth (2015) <doi:10.1007/978-3-319-20807-7_36>; H. Langseth, T.D. Nielsen, I. Pérez-Bernabé, A. Salmerón (2014) <doi:10.1016/j.ijar.2013.09.012>; I. Pérez-Bernabé, A. Fernández, R. Rumí, A. Salmerón (2016) <doi:10.1007/s10618-015-0429-7>). The package provides functionality for learning univariate, multivariate and conditional densities, with the possibility of incorporating prior knowledge. Structural learning of hybrid Bayesian networks is also provided. A set of useful tools is provided, including plotting, printing and likelihood evaluation. This package makes use of S3 objects, with two new classes called 'motbf' and 'jointmotbf'.

Ana D. Maldonado

MoTBFs

Learning Hybrid Bayesian Networks using Mixtures of Truncated
Basis Functions

Inmaculada Pérez-Bernabé

Antonio Salmerón

Thomas D.  Nielsen

Ángel T.  Sáez-Ruiz

predict.motbf_fit function

<dl><dt>object</dt>
<dd>An object of class "motbf_fit"</dd>
<dt>target</dt>
<dd>A character string indicating the variable to be predicted</dd>
<dt>data</dt>
<dd>A data frame containing the predictive variables</dd>
<dt>method</dt>
<dd>A character string indicating the method to perform the inference. Options are NULL, "ve" (variable elimination) and "fs" (forward sampling).</dd>
<dt>prob</dt>
<dd>A logical value. If TRUE, the probabilities used for classification are returned as an attribute.</dd>
<dt>parallel</dt>
<dd>A logical value. If TRUE, parallelization is carried out.</dd>
<dt>...</dt>
<dd>Additional arguments used if method = "fs".</dd></dl>

Arguments

Predict from an MoTBF Bayesian Network — predict.motbf_fit

<dl>

<dt>object</dt>
<dd>An object of class "motbf_fit"</dd>


<dt>target</dt>
<dd>A character string indicating the variable to be predicted</dd>


<dt>data</dt>
<dd>A data frame containing the predictive variables</dd>


<dt>method</dt>
<dd>A character string indicating the method to perform the inference. Options are NULL, "ve" (variable elimination) and "fs" (forward sampling).</dd>


<dt>prob</dt>
<dd>A logical value. If TRUE, the probabilities used for classification are returned as an attribute.</dd>


<dt>parallel</dt>
<dd>A logical value. If TRUE, parallelization is carried out.</dd>


<dt>...</dt>
<dd>Additional arguments used if method = "fs".</dd>

</dl>

predict.motbf_fit: Predict from an MoTBF Bayesian Network

Description

Usage

Arguments