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

Packages that use Measure
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
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.