Package | Description |
---|---|
com.bayesserver.analysis | |
com.bayesserver.inference | |
com.bayesserver.learning.parameters | |
com.bayesserver.learning.structure | |
com.bayesserver.optimization |
Modifier and Type | Method and Description |
---|---|
InferenceFactory |
ImpactOptions.getFactory()
Gets the inference factory which is used to create inference engines during an impact analysis.
|
InferenceFactory |
LogLikelihoodAnalysisOptions.getFactory()
Gets the inference factory which is used to create inference engines during a Log-Likelihood analysis.
|
InferenceFactory |
AssociationOptions.getInferenceFactory()
Gets the inference factory used for link strength calculations.
|
InferenceFactory |
AutoInsightOptions.getInferenceFactory()
Gets the inference factory used for link strength calculations.
|
InferenceFactory |
ClusterCountOptions.getInferenceFactory()
Gets the factory which is used to create inference engines during the cluster count tests.
|
InferenceFactory |
InSampleAnomalyDetectionOptions.getInferenceFactory()
Gets the factory which is used to create inference engines during the in-sample anomaly detection process.
|
Modifier and Type | Method and Description |
---|---|
static AutoInsightOutput |
AutoInsight.calculate(State target,
List<Variable> testVariables,
InferenceFactory factory)
Uses comparison queries to automatically derive insight about a target variable from a trained network.
|
static AutoInsightOutput |
AutoInsight.calculate(State target,
List<Variable> testVariables,
InferenceFactory factory,
Evidence evidence)
Uses comparison queries to automatically derive insight about a target variable from a trained network.
|
static ValueOfInformationOutput |
ValueOfInformation.calculate(VariableContext hypothesis,
List<VariableContext> testVariables,
Evidence evidence,
InferenceFactory factory,
ValueOfInformationOptions options)
Calculates value of information, which can be used to determine which variables are most likely to reduce the uncertainty of a particular variable.
|
static ValueOfInformationOutput |
ValueOfInformation.calculate(Variable hypothesis,
List<Variable> testVariables,
Evidence evidence,
InferenceFactory factory,
ValueOfInformationOptions options)
Calculates value of information, which can be used to determine which variables are most likely to reduce the uncertainty of a particular variable.
|
void |
ImpactOptions.setFactory(InferenceFactory value)
Sets the inference factory which is used to create inference engines during an impact analysis.
|
void |
LogLikelihoodAnalysisOptions.setFactory(InferenceFactory value)
Sets the inference factory which is used to create inference engines during a Log-Likelihood analysis.
|
void |
AssociationOptions.setInferenceFactory(InferenceFactory value)
Sets the inference factory used for link strength calculations.
|
void |
AutoInsightOptions.setInferenceFactory(InferenceFactory value)
Sets the inference factory used for link strength calculations.
|
void |
ClusterCountOptions.setInferenceFactory(InferenceFactory value)
Sets the factory which is used to create inference engines during the cluster count tests.
|
void |
InSampleAnomalyDetectionOptions.setInferenceFactory(InferenceFactory value)
Sets the factory which is used to create inference engines during the in-sample anomaly detection process.
|
Constructor and Description |
---|
SensitivityToParameters(Network network,
InferenceFactory factory)
Initializes a new instance of the
SensitivityToParameters class . |
Modifier and Type | Class and Description |
---|---|
class |
LikelihoodSamplingInferenceFactory
Uses the factory design pattern to create inference related objects for the Likelihood Sampling algorithm.
|
class |
LoopyBeliefInferenceFactory
Uses the factory design pattern to create inference related objects for the Loopy Belief algorithm.
|
class |
RelevanceTreeInferenceFactory
Uses the factory design pattern to create inference related objects for the Relevance Tree algorithm.
|
class |
VariableEliminationInferenceFactory
Uses the factory design pattern to create inference related objects for the Variable elimination algorithm.
|
Modifier and Type | Method and Description |
---|---|
static void |
ParameterLearning.learnDistributedMapper(EvidencePartition<DistributedMapperContext> partition,
NameValuesReader configuration,
NameValuesWriter output,
InferenceFactory factory)
This method should be called during distributed parameter learning on a distributed partition.
|
Constructor and Description |
---|
OnlineLearning(Network network,
InferenceFactory factory)
Initializes a new instance of the
OnlineLearning class. |
ParameterLearning(Network network,
InferenceFactory factory)
Initializes a new instance of the
ParameterLearning class. |
Modifier and Type | Method and Description |
---|---|
InferenceFactory |
ClusteringStructuralLearningOptions.getInferenceFactory()
Gets the inference factory used during scoring.
|
InferenceFactory |
HierarchicalStructuralLearningOptions.getInferenceFactory()
Gets the inference factory used during scoring.
|
InferenceFactory |
SearchStructuralLearningOptions.getInferenceFactory()
Gets the inference factory used during scoring.
|
Modifier and Type | Method and Description |
---|---|
void |
ClusteringStructuralLearningOptions.setInferenceFactory(InferenceFactory value)
Sets the inference factory used during scoring.
|
void |
HierarchicalStructuralLearningOptions.setInferenceFactory(InferenceFactory value)
Sets the inference factory used during scoring.
|
void |
SearchStructuralLearningOptions.setInferenceFactory(InferenceFactory value)
Sets the inference factory used during scoring.
|
Modifier and Type | Method and Description |
---|---|
InferenceFactory |
GeneticOptionsBase.getInferenceFactory()
Used to create one or more inference engines, used by the algorithm to determine the fitness of possible solutions.
|
InferenceFactory |
OptimizerOptions.getInferenceFactory()
Creates one or more inference engines used by the optimization algorithm.
|
Modifier and Type | Method and Description |
---|---|
void |
GeneticOptionsBase.setInferenceFactory(InferenceFactory value)
Used to create one or more inference engines, used by the algorithm to determine the fitness of possible solutions.
|
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