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

Packages that use Measure

Package	Description
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels	Provides DifferentiableStatisticalModels 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

Modifier and Type	Field and Description
protected Measure	BayesianNetworkDiffSM.structureMeasure Measure that defines the network structure.

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels that return Measure

Modifier and Type	Method and Description
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

Constructor and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Method and Description
Measure	Measure.clone()

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures that return types with arguments of type Measure

Modifier and Type	Method and Description
InstanceParameterSet<Measure>	Measure.getCurrentParameterSet()

Constructor parameters in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures with type arguments of type Measure

Constructor and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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.