Modifier and Type | Method and Description |
---|---|
static AssociationOutput |
Association.calculate(List<AssociationPair> pairs,
Evidence evidence,
AssociationOptions options)
Calculates the association/information between two sets of variables, such as those at either end of a Link.
|
static ImpactOutput |
Impact.calculate(Network network,
Distribution hypothesisQuery,
Evidence evidence,
List<Variable> evidenceToAnalyse,
ImpactOptions options)
Analyzes the impact of sets of evidence on the resulting probability distribution of a hypothesis variable.
|
static ImpactOutput |
Impact.calculate(Network network,
Distribution hypothesisQuery,
StateContext[] hypothesisCombination,
Evidence evidence,
List<Variable> evidenceToAnalyse,
ImpactOptions options)
Analyzes the impact of sets of evidence on a hypothesis query and discrete combination of that hypothesis query.
|
static LogLikelihoodAnalysisOutput |
LogLikelihoodAnalysis.calculate(Network network,
Evidence evidence,
List<Variable> evidenceToAnalyse,
LogLikelihoodAnalysisOptions options)
Analyzes the log-likelihood based on subsets of evidence.
|
static DSeparationOutput |
DSeparation.calculate(Network network,
List<Node> sourceNodes,
List<Integer> sourceNodeTimes,
List<Node> testNodes,
List<Integer> testTimes,
Evidence evidence,
DSeparationOptions options)
Calculates whether sets of nodes are D-Separated, given any evidence, and associated times for any temporal nodes.
|
static DSeparationOutput |
DSeparation.calculate(Network network,
List<Node> sourceNodes,
List<Node> testNodes,
Evidence evidence,
DSeparationOptions options)
Calculates whether sets of nodes are D-Separated, given any evidence.
|
static ImpactOutput |
Impact.calculate(Network network,
Variable hypothesisVariable,
Evidence evidence,
List<Variable> evidenceToAnalyse,
ImpactOptions options)
Analyzes the impact of sets of evidence on a hypothesis state and its variable.
|
static ImpactOutput |
Impact.calculate(Network network,
Variable hypothesisVariable,
State hypothesisState,
Evidence evidence,
List<Variable> evidenceToAnalyse,
ImpactOptions options)
Analyzes the impact of sets of evidence on a hypothesis state and its variable.
|
static AutoInsightOutput |
AutoInsight.calculate(State target,
List<Variable> testVariables,
Evidence evidence,
AutoInsightOptions options)
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 AutoInsightOutput[] |
AutoInsight.calculate(Variable continuousTarget,
List<Interval<Double>> targetIntervals,
List<Variable> testVariables,
Evidence evidence,
AutoInsightOptions options)
Uses comparison queries to automatically derive insight about a target variable from a trained network.
|
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.
|
static ImpactHypothesisOutput |
Impact.calculateStreamed(Network network,
Distribution hypothesisQuery,
Evidence evidence,
List<Variable> evidenceToAnalyse,
ImpactAction outputItem,
ImpactOptions options)
Analyzes the impact of sets of evidence on the resulting probability distribution of a hypothesis variable.
|
static ImpactHypothesisOutput |
Impact.calculateStreamed(Network network,
Distribution hypothesisQuery,
StateContext[] hypothesisState,
Evidence evidence,
List<Variable> evidenceToAnalyse,
ImpactAction outputItem,
ImpactOptions options)
Analyzes the impact of sets of evidence on a hypothesis query and discrete combination of that hypothesis query.
|
static LogLikelihoodAnalysisBaselineOutput |
LogLikelihoodAnalysis.calculateStreamed(Network network,
Evidence evidence,
List<Variable> evidenceToAnalyse,
LogLikelihoodAnalysisAction outputItem,
LogLikelihoodAnalysisOptions options)
Analyzes the log-likelihood based on subsets of evidence.
|
SensitivityFunctionOneWay |
SensitivityToParameters.oneWay(Evidence evidence,
State hypothesis,
ParameterReference parameter)
Calculates how a hypothesis varies based on changes to a single parameter.
|
InSampleAnomalyDetectionOutput |
InSampleAnomalyDetection.test(Evidence evidence)
Determines whether a record is anomalous.
|
SensitivityFunctionTwoWay |
SensitivityToParameters.twoWay(Evidence evidence,
State hypothesis,
ParameterReference parameter1,
ParameterReference parameter2)
Calculates how a hypothesis varies based on changes to two parameters.
|
Modifier and Type | Method and Description |
---|---|
Evidence |
CausalInferenceBase.getBaseEvidence()
Optional evidence which can be used to calculate the lift of queries.
|
Evidence |
CausalInferenceBase.getEvidence()
Represents the evidence, or case data (e.g.
|
Modifier and Type | Method and Description |
---|---|
static EffectsAnalysisOutput |
EffectsAnalysis.calculate(Variable treatment,
Variable outcome,
CausalEffectKind effect,
Evidence fixedEvidence,
InferenceFactory factory,
EffectsAnalysisOptions options)
Calculate the causal effect on a target, varying for different treatment values.
|
static void |
BackdoorGraph.convert(Network network,
Evidence evidence,
Distribution query,
BackdoorGraphOptions options)
Constructs the Backdoor graph or the proper Backdoor graph from a Bayesian network, one of more treatments (X) and one or more outcomes (Y).
|
static void |
IndirectGraph.convert(Network network,
Evidence evidence,
Distribution query,
IndirectGraphOptions options)
Constructs the 'Indirect graph' from a Bayesian network, one of more treatments (X) and one or more outcomes (Y).
|
IdentificationOutput |
BackdoorCriterion.identify(Evidence evidence,
Distribution query,
IdentificationOptions options)
Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
|
IdentificationOutput |
DisjunctiveCauseCriterion.identify(Evidence evidence,
Distribution query,
IdentificationOptions options)
Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
|
IdentificationOutput |
FrontDoorCriterion.identify(Evidence evidence,
Distribution query,
IdentificationOptions options)
Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
|
IdentificationOutput |
Identification.identify(Evidence evidence,
Distribution query,
IdentificationOptions options)
Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
|
BackdoorCriterionOutput |
FrontDoorCriterion.identifyXZ(Evidence evidence,
FrontDoorSet frontDoorNodes,
BackdoorCriterionOptions options)
Uses the 'Backdoor criterion' to identify any 'adjustment sets' between treatments (X) and front-door nodes (Z).
|
BackdoorCriterionOutput |
FrontDoorCriterion.identifyZY(Evidence evidence,
FrontDoorSet frontDoorNodes,
Distribution query,
BackdoorCriterionOptions options)
Uses the 'Backdoor criterion' to identify any 'adjustment sets' between front-door nodes (Z) and outcomes (Y).
|
boolean |
BackdoorCriterion.isValid(Evidence evidence,
Distribution query,
ValidationOptions options)
Tests whether adjustment inputs are valid, without raising an exception.
|
boolean |
DisjunctiveCauseCriterion.isValid(Evidence evidence,
Distribution query,
ValidationOptions options)
Tests whether adjustment inputs are valid, without raising an exception.
|
boolean |
FrontDoorCriterion.isValid(Evidence evidence,
Distribution query,
ValidationOptions options)
Tests whether adjustment inputs are valid, without raising an exception.
|
boolean |
Validation.isValid(Evidence evidence,
Distribution query,
ValidationOptions options)
Tests whether adjustment inputs are valid, without raising an exception.
|
void |
CausalInferenceBase.setBaseEvidence(Evidence value)
Optional evidence which can be used to calculate the lift of queries.
|
void |
CausalInferenceBase.setEvidence(Evidence value)
Represents the evidence, or case data (e.g.
|
static void |
Abduction.update(Evidence evidence,
List<Variable> abductionEvidenceVariables,
List<Variable> characteristicVariables,
AbductionOptions options)
Performs abduction which is one of the steps in 'counterfactual analysis'.
|
void |
BackdoorCriterion.validate(Evidence evidence,
Distribution query,
ValidationOptions options)
Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.
|
void |
DisjunctiveCauseCriterion.validate(Evidence evidence,
Distribution query,
ValidationOptions options)
Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.
|
void |
FrontDoorCriterion.validate(Evidence evidence,
Distribution query,
ValidationOptions options)
Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.
|
void |
Validation.validate(Evidence evidence,
Distribution query,
ValidationOptions options)
Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.
|
Modifier and Type | Method and Description |
---|---|
boolean |
DefaultEvidenceReader.read(Evidence evidence,
ReadOptions readOptions)
Reads the next case (record).
|
boolean |
EvidenceReader.read(Evidence evidence,
ReadOptions readOptions)
Reads the next case (record).
|
boolean |
DefaultEvidenceReader.readTemporal(Evidence evidence,
ReadOptions readOptions)
Reads the next temporal record, setting evidence.
|
Modifier and Type | Method and Description |
---|---|
void |
DataSampler.setFixedData(Evidence value)
Sets any evidence that should be fixed for each sample.
|
void |
DataSampler.takeSample(Evidence sampleData,
RandomNumberGenerator random,
DataSamplingOptions options)
Generates sample data from the Bayesian network or Dynamic Bayesian network.
|
Constructor and Description |
---|
DataSampler(Network network,
Evidence fixedData)
Initializes a new instance of the
DataSampler class. |
Modifier and Type | Class and Description |
---|---|
class |
DefaultEvidence
Represents the evidence, or case data (e.g.
|
Modifier and Type | Method and Description |
---|---|
Evidence |
Inference.getBaseEvidence()
Optional evidence which can be used to calculate the lift of queries.
|
Evidence |
LikelihoodSamplingInference.getBaseEvidence()
Optional evidence which can be used to calculate the lift of queries.
|
Evidence |
LoopyBeliefInference.getBaseEvidence()
Optional evidence which can be used to calculate the lift of queries.
|
Evidence |
RelevanceTreeInference.getBaseEvidence()
Optional evidence which can be used to calculate the lift of queries.
|
Evidence |
VariableEliminationInference.getBaseEvidence()
Optional evidence which can be used to calculate the lift of queries.
|
Evidence |
Inference.getEvidence()
Represents the evidence, or case data (e.g.
|
Evidence |
LikelihoodSamplingInference.getEvidence()
Represents the evidence, or case data (e.g.
|
Evidence |
LoopyBeliefInference.getEvidence()
Represents the evidence, or case data (e.g.
|
Evidence |
RelevanceTreeInference.getEvidence()
Represents the evidence, or case data (e.g.
|
Evidence |
VariableEliminationInference.getEvidence()
Gets the evidence (case data, e.g.
|
Modifier and Type | Method and Description |
---|---|
void |
DefaultEvidence.copy(Evidence evidence)
Replaces the current evidence, with that from another
Evidence instance. |
void |
Evidence.copy(Evidence evidence)
Replaces the current evidence, with that from another
Evidence instance. |
void |
DefaultEvidence.copy(Evidence evidence,
Variable variable)
Replaces the current evidence for an individual variable, with that from another
Evidence instance. |
void |
Evidence.copy(Evidence evidence,
Variable variable)
Replaces the current evidence for an individual variable, with that from another
Evidence instance. |
void |
DefaultEvidence.copy(Evidence evidence,
Variable variable,
Integer time)
Replaces the current evidence for an individual variable at a specific time, with that from another
Evidence instance. |
void |
Evidence.copy(Evidence evidence,
Variable variable,
Integer time)
Replaces the current evidence for an individual variable at a specific time, with that from another
Evidence instance. |
static TreeQueryOutput |
TreeQuery.query(Network network,
QueryDistributionCollection queryDistributions,
Evidence evidence,
TreeQueryOptions queryOptions)
Calculates properties of a Bayesian network or Dynamic Bayesian network when converted to a tree for inference.
|
void |
Inference.setBaseEvidence(Evidence value)
Optional evidence which can be used to calculate the lift of queries.
|
void |
LikelihoodSamplingInference.setBaseEvidence(Evidence value)
Optional evidence which can be used to calculate the lift of queries.
|
void |
LoopyBeliefInference.setBaseEvidence(Evidence value)
Optional evidence which can be used to calculate the lift of queries.
|
void |
RelevanceTreeInference.setBaseEvidence(Evidence value)
Optional evidence which can be used to calculate the lift of queries.
|
void |
VariableEliminationInference.setBaseEvidence(Evidence value)
Optional evidence which can be used to calculate the lift of queries.
|
void |
Inference.setEvidence(Evidence value)
Represents the evidence, or case data (e.g.
|
void |
LikelihoodSamplingInference.setEvidence(Evidence value)
Represents the evidence, or case data (e.g.
|
void |
LoopyBeliefInference.setEvidence(Evidence value)
Represents the evidence, or case data (e.g.
|
void |
RelevanceTreeInference.setEvidence(Evidence value)
Represents the evidence, or case data (e.g.
|
void |
VariableEliminationInference.setEvidence(Evidence value)
Sets the evidence (case data, e.g.
|
Constructor and Description |
---|
DefaultEvidence(Evidence evidence)
Initializes a new instance of the
DefaultEvidence class, and copies the evidence from another instance. |
Modifier and Type | Method and Description |
---|---|
Evidence |
OnlineLearning.getEvidence()
Gets the evidence used internally.
|
Modifier and Type | Method and Description |
---|---|
void |
OnlineLearning.adapt(Evidence evidence,
OnlineLearningOptions options)
Adapt the parameters of a Bayesian network using Bayesian statistics.
|
Modifier and Type | Method and Description |
---|---|
Evidence |
GeneticOptimizerOutput.getEvidence()
The evidence required to produce the optimized objective value.
|
Evidence |
GeneticOptimizerProgressInfo.getEvidence()
Gets the evidence for the objective value.
|
Evidence |
GeneticSimplificationOutput.getEvidence()
The evidence required to produce the optimized objective value.
|
Evidence |
OptimizerOutput.getEvidence()
The evidence required to produce the optimized objective value.
|
Evidence |
OptimizerProgressInfo.getEvidence()
Gets the evidence for the objective value.
|
Evidence |
GeneticSimplificationOptions.getEvidenceToSimplify()
The evidence from a previous optimization.
|
Modifier and Type | Method and Description |
---|---|
OptimizerOutput |
GeneticOptimizer.optimize(Network network,
Objective objective,
List<DesignVariable> designVariables,
Evidence fixedEvidence,
OptimizerOptions options)
Perform optimization of an objective (target).
|
OptimizerOutput |
GeneticSimplification.optimize(Network network,
Objective objective,
List<DesignVariable> designVariables,
Evidence fixedEvidence,
OptimizerOptions options)
Perform optimization of an objective (target).
|
OptimizerOutput |
Optimizer.optimize(Network network,
Objective objective,
List<DesignVariable> designVariables,
Evidence fixedEvidence,
OptimizerOptions options)
Perform optimization of an objective (target).
|
void |
GeneticSimplificationOptions.setEvidenceToSimplify(Evidence value)
The evidence from a previous optimization.
|
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