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Packages that use Measure | |
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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 |
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Fields in de.jstacs.scoringFunctions.directedGraphicalModels declared as Measure | |
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protected Measure |
BayesianNetworkScoringFunction.structureMeasure
Measure that defines the network structure. |
Methods in de.jstacs.scoringFunctions.directedGraphicalModels that return Measure | |
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Measure |
BayesianNetworkScoringFunctionParameterSet.getMeasure()
Returns the structure Measure defined by this set of parameters. |
Constructors in de.jstacs.scoringFunctions.directedGraphicalModels with parameters of type Measure | |
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BayesianNetworkScoringFunction(AlphabetContainer alphabet,
int length,
double ess,
boolean plugInParameters,
Measure structureMeasure)
Creates a new BayesianNetworkScoringFunction that has neither
been initialized nor trained. |
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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 |
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Subclasses of Measure in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures | |
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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 | |
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Measure |
Measure.clone()
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Uses of Measure in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures |
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Subclasses of Measure in de.jstacs.scoringFunctions.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
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 |
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Subclasses of Measure in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures | |
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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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