Class | Description |
---|---|
Association |
Calculates the strength between pairs of variables or sets of variables.
|
AssociationOptions |
Options that affect the link strength algorithm.
|
AssociationOutput |
Contains the results of an Association analysis.
|
AssociationPair |
Defines two sets of variables to be analyzed by the Association algorithm.
|
AssociationPairOutput |
Contains the results of the association calculations between two sets of variables.
|
AutoInsight |
Uses comparison queries to automatically derive insight about a target variable from a trained network.
|
AutoInsightOptions |
Options that affect auto-insight calculations.
|
AutoInsightOutput |
Contains the results obtained from
AutoInsight . |
AutoInsightSamplingOptions |
Options that affect any sampling required during auto-insight calculations.
|
AutoInsightStateOutput |
Contains the results obtained from
AutoInsight for each test variable. |
AutoInsightStateOutputCollection |
Represents a collection of
AutoInsightStateOutput instances. |
AutoInsightVariableOutput |
Represents the output obtained from
AutoInsight for a test variable. |
AutoInsightVariableOutputCollection |
Represents a collection of
AutoInsightVariableOutput instances. |
ClusterCount |
Methods to determine the number of clusters (discrete states of a latent variable).
|
ClusterCountOptions |
Options used by
ClusterCount . |
ClusterCountOutput |
Output information returned from
ClusterCount . |
ClusterScore |
Contains the results of a cluster configuration returned from
ClusterCount . |
CombinationOptions |
Determines which combinations are generated by
Combinations . |
Combinations |
Generates the available state combinations for a set of variables or counts.
|
ConfusionMatrix |
Calculates a confusion matrix for a network which is used to predict discrete values (classification).
|
ConfusionMatrixCell |
Contains statistics about a cell in a
ConfusionMatrix . |
Correlation |
Methods to convert covariance matrices to correlation matrices.
|
DSeparation |
Contains methods to calculate D-Separation.
|
DSeparationOptions |
Options for calculating D-Separation.
|
DSeparationOutput |
Contains the results of a test for D-Separation.
|
DSeparationTestResult |
The result of a D-Separation check for a test node.
|
DSeparationTestResultCollection |
Collection of D-Separation test results.
|
HistogramDensity |
Represents an empirical density function built from a histogram, which can represent arbitrary univariate distributions.
|
HistogramDensityOptions |
Options for learning a histogram based empirical density.
|
Impact |
Analyzes the impact of evidence.
|
ImpactHypothesisOutput |
Output information about the hypothesis variable/state from an Impact analysis.
|
ImpactOptions |
Options affecting how Impact analysis calculations are performed.
|
ImpactOutput |
Contains the results of an Impact analysis.
|
ImpactOutputItem |
The output from an impact analysis, for a particular subset of evidence.
|
InSampleAnomalyDetection |
Detects in-sample anomalies in a data set.
|
InSampleAnomalyDetectionOptions |
Options used by
InSampleAnomalyDetection . |
InSampleAnomalyDetectionOutput |
Output used by
InSampleAnomalyDetection . |
LiftChart |
Represents a lift chart, used to measure predictive performance.
|
LiftChartPoint |
Represents an XY coordinate in a lift chart.
|
LogLikelihoodAnalysis |
Analyzes the log-likelihood for different evidence subsets.
|
LogLikelihoodAnalysisBaselineOutput |
Output information about the log-likelihood from a log-likelihood analysis.
|
LogLikelihoodAnalysisOptions |
Options affecting how Log-Likelihood analysis calculations are performed.
|
LogLikelihoodAnalysisOutput |
Contains the results of a Log-Likelihood analysis.
|
LogLikelihoodAnalysisOutputItem |
The output from a Log-Likelihood analysis, for a particular subset of evidence.
|
ParameterReference |
References a parameter in a node distribution.
|
ParameterTuning |
Calculates how a parameter can be updated so that the resulting value of a hypothesis is within a given range.
|
ParameterTuningOneWay |
Represents the result of one way parameter tuning.
|
RegressionStatistics |
Calculates statistics for a network which is used to predict continuous values (regression).
|
SensitivityFunctionOneWay |
Represents the result on a one-way sensitivity to parameters analysis.
|
SensitivityFunctionTwoWay |
Represents the result on a two-way sensitivity to parameters analysis.
|
SensitivityToParameters |
Calculates the affect of one or more parameters on the value of a hypothesis.
|
ValueOfInformation |
Contains methods to determine what new evidence is most likely to reduce the uncertainty of a variable.
|
ValueOfInformationOptions |
Options for calculating
ValueOfInformation . |
ValueOfInformationOutput |
Contains the results of the tests carried out using
ValueOfInformation . |
ValueOfInformationTestOutput |
Contains information about a variable tested via
ValueOfInformation . |
Enum | Description |
---|---|
AutoInsightJSDivergence |
Determines the type of Jensen Shannon divergence calculations, if any, performed during an auto insight analysis.
|
AutoInsightKLDivergence |
Determines the type of KL divergence calculations, if any, performed during an auto insight analysis.
|
DSeparationCategory |
The result of a D-Separation test.
|
ImpactSubsetMethod |
Determines how subsets are determined during impact analysis.
|
LogLikelihoodAnalysisSubsetMethod |
Determines how subsets are determined during a Log-Likelihood analysis.
|
ValueOfInformationKind |
The type of value of information statistic calculated.
|
Exception | Description |
---|---|
ConstraintNotSatisfiedException |
Exception raised when parameter tuning attempts to solve for a constraint that cannot be satisfied by the change(s) in parameters.
|
ConstraintSatisfiedException |
Exception raised when parameter tuning attempts to solve for a constraint that is already true.
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