Package de.jstacs.scoringFunctions.directedGraphicalModels

Provides ScoringFunctions that are equivalent to directed graphical models.

See:
Description

Class Summary

BayesianNetworkScoringFunction	This class implements a scoring function that is a moral directed graphical model, i.e. a moral Bayesian network.
BayesianNetworkScoringFunctionParameterSet	Class for the parameters of a BayesianNetworkScoringFunction.
MutableMarkovModelScoringFunction	This class implements a AbstractNormalizableScoringFunction for an inhomogeneous Markov model.
Parameter	Class for the parameters of a BayesianNetworkScoringFunction.
ParameterTree	Class for the tree that represents the context of a Parameter in a BayesianNetworkScoringFunction.

Package de.jstacs.scoringFunctions.directedGraphicalModels Description

Provides ScoringFunctions that are equivalent to directed graphical models. The currently implemented BayesianNetworkScoringFunction provides structure learning for inhomogeneous Markov models, Bayesian trees, and permuted Markov models, but can be extended to other graph structures easily using the Measure interface. When using a BayesianNetworkScoringFunction in the CLLClassifier, it is important to define only moral Bayesian network structures, because other in the chosen parameterization the normalization is not defined.