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## Package de.jstacs.scoringFunctions.directedGraphicalModels

Provides `ScoringFunction`s 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 `ScoringFunction`s 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.

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