getDAG

Get the Directed acyclic graph (DAG) from a Bayesian network (BN)

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

getDAG function

<dl><dt>bn</dt>
<dd>An object of class <code>motbf_fit</code> or <code>bn.fit</code> (from bnlearn package).</dd></dl>

Arguments

Retrieve DAG from BN — getDAG

<dl>

<dt>bn</dt>
<dd>An object of class <code>motbf_fit</code> or <code>bn.fit</code> (from bnlearn package).</dd>

</dl>

getDAG: Retrieve DAG from BN

Description

Usage

Value

Arguments