MoTBFs - Learning Hybrid Bayesian Networks using Mixtures of Truncated
Basis Functions
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'.
