thyroid

<p>This data set if one of the several databases about Thyroid avalaible at the UCI repository. 
The task is to detect is a given patient is normal (1) or suffers from hyperthyroidism (2) 
or hypothyroidism (3) .</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'.

Ana D. Maldonado

MoTBFs

Learning Hybrid Bayesian Networks using Mixtures of Truncated
Basis Functions

thyroid function

<p>A data frame with 7200 rows, 21 variables and the class.</p>

thyroid: Data set Thyroid Disease (thyroid0387)

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