Class BicDiffPerRecord
java.lang.Object
edu.cmu.tetrad.algcomparison.statistic.BicDiffPerRecord
- All Implemented Interfaces:
Statistic
,Serializable
Difference between the true and estiamted BIC scores.
- Version:
- $Id: $Id
- Author:
- josephramsey
- See Also:
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionThe abbreviation for the statistic.Returns a short one-line description of this statistic.double
getNormValue
(double value) Returns a mapping of the statistic to the interval [0, 1], with higher being better.double
Returns the value of this statistic, given the true graph and the estimated graph.void
setPrecomputeCovariances
(boolean precomputeCovariances) Returns true if the covariances are precomputed.
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Constructor Details
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BicDiffPerRecord
public BicDiffPerRecord()Constructs a new instance of the statistic.
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Method Details
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getAbbreviation
The abbreviation for the statistic. This will be printed at the top of each column.- Specified by:
getAbbreviation
in interfaceStatistic
- Returns:
- This abbreviation.
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getDescription
Returns a short one-line description of this statistic. This will be printed at the beginning of the report.- Specified by:
getDescription
in interfaceStatistic
- Returns:
- This description.
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getValue
Returns the value of this statistic, given the true graph and the estimated graph.Returns the difference between the true and estimated BIC scores, divided by the sample size.
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getNormValue
public double getNormValue(double value) Returns a mapping of the statistic to the interval [0, 1], with higher being better. This is used for a calculation of a utility for an algorithm. If the statistic is already between 0 and 1, you can just return the statistic.Returns the normalized value of the statistic.
- Specified by:
getNormValue
in interfaceStatistic
- Parameters:
value
- The value of the statistic.- Returns:
- The weight of the statistic, 0 to 1, higher is better.
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setPrecomputeCovariances
public void setPrecomputeCovariances(boolean precomputeCovariances) Returns true if the covariances are precomputed.- Parameters:
precomputeCovariances
- True if the covariances are precomputed.
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