MoTBFs v1.2


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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. 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'.

Functions in MoTBFs

Name Description
coef.jointmotbf Extract Coefficients of a "jointmotbf" Object
findConditional Find Fitted Conditional MoTBFs
integralMTE Integral MTE
Subclass-MoTBF Subclass "motbf" Functions
conditionalmotbf.learning Learning Conditional Functions
derivMoTBF Derivative MoTBF
forward_sampling Forward Sampling
coefExpJointCDF Degree Function
derivMTE Derivative MTE
as.function.motbf Coerce an "motbf" Object to a Function
coef.mop Extract MOP Coefficients
asMTEString Parameters to MTE String
coef.motbf Extract MoTBF Coefficients
evalJointFunction Evaluate a Joint Function
clean Remove Objects from Memory
derivMOP Derivative MOP
integralJointMoTBF Integral Joint MoTBF
is.root Root nodes
asMOPString Parameters to MOP String
ecoli Data set Ecoli: Protein Localization Sites
coef.mte Extract MTE Coefficients
integralMoTBF Integral MoTBF
goodnessDiscreteVariables Goodness of discrete probabilities
printBN Prints BN Results
integralMOP Integral MOP
jointCDF Cumulative Joint Distribution
dataMining Functions to Manipulate a Dataset
preprocessedData Remove Missing Values in a Dataset by Rows
dimensionFunction Dimension of Functions
sample_MoTBFs Generate Samples From Conditional MoTBFs
jointmotbf.learning Learning Joint Functions
subsetData Subset a Dataset
nVariables Number of Variables in a Joint Function
getCoefficients Get the Coefficients
newRangePriorData Redefining the Domain
is.discrete Check discreteness of a node
discreteStatesFromBN Get the states of all discrete nodes from a MoTFB-BN
is.observed Observed Node
getNonNormalisedRandomMoTBF Ramdom MoTBF
rescaledFunctions Rescales an MoTBF Function
rnormMultiv Multivariate Normal Sample
generateNormalPriorData Prior Data
learnMoTBFpriorInformation Incorporating Prior Knowledge
plotConditional Plots for Conditional Functions
probDiscreteVariable Probabilities Discrete Variables
plot.motbf Plots for 'motbf' Objects Initialize Data Frame
marginalJointMoTBF Marginal Joint MoTBF
getChildParentsFromGraph Get Relationships in a Network
mop.learning Fitting Polynomial Models
printConditional Prints Conditional Functions
printDiscreteBN Prints Discrete Learnings
summary.jointmotbf Summary of a "jointmotbf" Object
summary.motbf Summary of an "motbf" Object
motbf_type Type of MoTBF
mte.learning Fitting Exponential Models
goodnessMoTBFBN BIC of an MoTBF BN
plot.jointmotbf Bidimensional Plots for 'jointmotbf' Objects
thyroid Data set Thyroid Disease (thyroid0387)
parentValues Value of Parent Nodes
univMoTBF Fitting MoTBFs
MoTBF-Distribution Random Generation for MoTBFs
LearningHC Learning Hybric Bayesian Networks
MoTBFs_Learning Learning MoTBFs in a Network
BICMultiFunctions BIC for Multiple Functions
Class-JointMoTBF Class "jointmotbf"
Class-MoTBF Class "motbf"
BICMoTBF Computing the BIC Score of an MoTBF Function
UpperBoundLogLikelihood Upper Bound Loglikelihood
as.function.jointmotbf Coerce a "jointmotbf" Object to a Function
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Type Package
Encoding UTF-8
License LGPL-3
NeedsCompilation yes
Repository CRAN
Packaged 2020-01-14 17:57:53 UTC; anamaldonado
Date/Publication 2020-01-14 19:00:02 UTC

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