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## Uses of Class
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure

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

Uses of Measure in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels

Fields in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels declared as Measure
`protected Measure` | `BayesianNetworkDiffSM.structureMeasure` `Measure` that defines the network structure.

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels that return Measure
`Measure` | `BayesianNetworkDiffSMParameterSet.getMeasure()` Returns the structure `Measure` defined by this set of parameters.

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

Uses of Measure in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures

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

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

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures that return types with arguments of type Measure
`InstanceParameterSet<Measure>` | `Measure.getCurrentParameterSet()`

Constructor parameters in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures with type arguments of type Measure
`Measure.MeasureParameterSet(Class<? extends Measure> clazz)` Creates a new empty `Measure.MeasureParameterSet` for the given sub-class of `Measure`,

Uses of Measure in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures

Subclasses of Measure in de.jstacs.sequenceScores.statisticalModels.differentiable.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 `BayesianNetworkDiffSM` .
`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 `BayesianNetworkDiffSM` .

Uses of Measure in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures

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

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