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) ; H. Langseth, T.D. Nielsen, I. Pérez-Bernabé, A. Salmerón (2014) ; I. Pérez-Bernabé, A. Fernández, R. Rumí, A. Salmerón (2016) ). 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'.


Reference manual

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1.4 by Ana D. Maldonado, 2 months ago

Browse source code at https://github.com/cran/MoTBFs

Authors: Inmaculada Pérez-Bernabé , Antonio Salmerón , Thomas D. Nielsen , Ana D. Maldonado

Documentation:   PDF Manual  

LGPL-3 license

Imports quadprog, lpSolve, bnlearn, methods, ggm, Matrix

See at CRAN