asMOPString

<p>A <code>"numeric"</code> vector containing the coefficients.</p>

parameters

<p>This function builds a string with the structure of a <code>'mop'</code> function.</p>

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'.

asMOPString: Parameters to MOP String

Description

Usage

Arguments

Value

Examples