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## Uses of Class
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.Measure

Packages that use Measure
de.jstacs.scoringFunctions.directedGraphicalModels | Provides `ScoringFunction`s that are equivalent to directed graphical models.
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures | Provides the facilities to learn the structure of a `BayesianNetworkScoringFunction`.
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures | Provides the facilities to learn the structure of a `BayesianNetworkScoringFunction` as a Bayesian tree using a number of measures to define a rating of structures
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures | Provides the facilities to learn the structure of a `BayesianNetworkScoringFunction` as a permuted Markov model using a number of measures to define a rating of structures

Uses of Measure in de.jstacs.scoringFunctions.directedGraphicalModels

Fields in de.jstacs.scoringFunctions.directedGraphicalModels declared as Measure
`protected Measure` | `BayesianNetworkScoringFunction.structureMeasure` `Measure` that defines the network structure.

Methods in de.jstacs.scoringFunctions.directedGraphicalModels that return Measure
`Measure` | `BayesianNetworkScoringFunctionParameterSet.getMeasure()` Returns the structure `Measure` defined by this set of parameters.

Constructors in de.jstacs.scoringFunctions.directedGraphicalModels with parameters of type Measure
`BayesianNetworkScoringFunction(AlphabetContainer alphabet, int length, double ess, boolean plugInParameters, Measure structureMeasure)` Creates a new `BayesianNetworkScoringFunction` that has neither been initialized nor trained.
`BayesianNetworkScoringFunctionParameterSet(AlphabetContainer alphabet, int length, double ess, boolean plugInParameters, Measure structureMeasure)` Creates a new `BayesianNetworkScoringFunctionParameterSet` with pre-defined parameter values.

Uses of Measure in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures

Subclasses of Measure in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures
`class` | `InhomogeneousMarkov` Class for a network structure of a `BayesianNetworkScoringFunction` that is an inhomogeneous Markov model.

Methods in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures that return Measure
`Measure` | `Measure.clone()`

Uses of Measure in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures

Subclasses of Measure in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures
`class` | `BTExplainingAwayResidual` Structure learning `Measure` that computes a maximum spanning tree based on the explaining away residual and uses the resulting tree structure as structure of a Bayesian tree (special case of a Bayesian network) in a `BayesianNetworkScoringFunction` .
`class` | `BTMutualInformation` Structure learning `Measure` that computes a maximum spanning tree based on mutual information and uses the resulting tree structure as structure of a Bayesian tree (special case of a Bayesian network) in a `BayesianNetworkScoringFunction` .

Uses of Measure in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures

Subclasses of Measure in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures
`class` | `PMMExplainingAwayResidual` Class for the network structure of a `BayesianNetworkScoringFunction` that is a permuted Markov model based on the explaining away residual.
`class` | `PMMMutualInformation` Class for the network structure of a `BayesianNetworkScoringFunction` that is a permuted Markov model based on mutual information.

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