getNonNormalisedRandomMoTBF

A <code>"numeric"</code> value containing the degree of the random function.

degree

A <code>"character"</code> string specifying the posibles potential
types, must be one of <code>"MOP"</code> or <code>"MTE"</code>.

POTENTIAL_TYPE

Get an MoTBF function ensuring positives values without being
a normalized function.

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. P<c3><a9>rez-Bernab<c3><a9>, A. Salmer<c3><b3>n, H. Langseth (2015) <doi:10.1007/978-3-319-20807-7_36>; H. Langseth, T.D. Nielsen, I. P<c3><a9>rez-Bernab<c3><a9>, A. Salmer<c3><b3>n (2014) <doi:10.1016/j.ijar.2013.09.012>; I. P<c3><a9>rez-Bernab<c3><a9>, A. Fern<c3><a1>ndez, R. Rum<c3><ad>, A. Salmer<c3><b3>n (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'.

getNonNormalisedRandomMoTBF: Ramdom MoTBF

Description

Usage

Arguments

Value

Examples