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Packages that use Measure | |
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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 |
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Fields in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels declared as Measure | |
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protected Measure |
BayesianNetworkDiffSM.structureMeasure
Measure that defines the network structure. |
Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels that return Measure | |
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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 | |
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BayesianNetworkDiffSM(AlphabetContainer alphabet,
int length,
double ess,
boolean plugInParameters,
Measure structureMeasure)
Creates a new BayesianNetworkDiffSM that has neither
been initialized nor trained. |
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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 |
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Subclasses of Measure in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures | |
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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 | |
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Measure |
Measure.clone()
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Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures that return types with arguments of type Measure | |
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InstanceParameterSet<Measure> |
Measure.getCurrentParameterSet()
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Constructor parameters in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures with type arguments of type Measure | |
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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 |
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Subclasses of Measure in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures | |
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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 |
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Subclasses of Measure in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures | |
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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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