gov.sandia.cognition.statistics.distribution

Class StudentTDistribution.CDF

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.statistics.AbstractDistribution<NumberType>

gov.sandia.cognition.statistics.AbstractClosedFormUnivariateDistribution<java.lang.Double>

gov.sandia.cognition.statistics.AbstractClosedFormSmoothUnivariateDistribution

gov.sandia.cognition.statistics.distribution.StudentTDistribution

gov.sandia.cognition.statistics.distribution.StudentTDistribution.CDF

All Implemented Interfaces:

Evaluator<java.lang.Double,java.lang.Double>, ClosedFormDifferentiableEvaluator<java.lang.Double,java.lang.Double,java.lang.Double>, DifferentiableEvaluator<java.lang.Double,java.lang.Double,java.lang.Double>, Vectorizable, ScalarFunction<java.lang.Double>, UnivariateScalarFunction, ClosedFormComputableDistribution<java.lang.Double>, ClosedFormCumulativeDistributionFunction<java.lang.Double>, ClosedFormDistribution<java.lang.Double>, ClosedFormUnivariateDistribution<java.lang.Double>, ComputableDistribution<java.lang.Double>, CumulativeDistributionFunction<java.lang.Double>, Distribution<java.lang.Double>, DistributionWithMean<java.lang.Double>, EstimableDistribution<java.lang.Double,StudentTDistribution>, InvertibleCumulativeDistributionFunction<java.lang.Double>, SmoothCumulativeDistributionFunction, SmoothUnivariateDistribution, UnivariateDistribution<java.lang.Double>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

StudentTDistribution

public static class StudentTDistribution.CDF
extends StudentTDistribution
implements SmoothCumulativeDistributionFunction, InvertibleCumulativeDistributionFunction<java.lang.Double>

Evaluator that computes the Cumulative Distribution Function (CDF) of a Student-t distribution with a fixed number of degrees of freedom

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class gov.sandia.cognition.statistics.distribution.StudentTDistribution

StudentTDistribution.CDF, StudentTDistribution.MaximumLikelihoodEstimator, StudentTDistribution.PDF, StudentTDistribution.WeightedMaximumLikelihoodEstimator

Field Summary

Fields inherited from class gov.sandia.cognition.statistics.distribution.StudentTDistribution

DEFAULT_DEGREES_OF_FREEDOM, DEFAULT_MEAN, DEFAULT_PRECISION, degreesOfFreedom, mean, precision

Constructor Summary

Constructors

Constructor and Description
CDF() Default constructor.
CDF(double degreesOfFreedom) Creates a new instance of CDF
CDF(double degreesOfFreedom, double mean, double precision) Creates a new instance of PDF
CDF(StudentTDistribution other) Creates a new instance of CDF

Method Summary

Modifier and Type	Method and Description
java.lang.Double	differentiate(java.lang.Double input) Differentiates the output with respect to the input
double	evaluate(double input) Produces a double output for the given double input
java.lang.Double	evaluate(java.lang.Double input) Evaluates the function on the given input and returns the output.
double	evaluateAsDouble(java.lang.Double input) Evaluates the scalar function as a double.
StudentTDistribution.CDF	getCDF() Gets the CDF of a scalar distribution.
StudentTDistribution.PDF	getDerivative() Gets the closed-form derivative of the function.
java.lang.Double	inverse(double p) Evaluates the Inverse Student-t CDF for the given probability and degrees of freedom

Methods inherited from class gov.sandia.cognition.statistics.distribution.StudentTDistribution

clone, convertFromVector, convertToVector, getDegreesOfFreedom, getEstimator, getMaxSupport, getMeanAsDouble, getMinSupport, getPrecision, getProbabilityFunction, getVariance, sampleInto, setDegreesOfFreedom, setMean, setPrecision, toString

Methods inherited from class gov.sandia.cognition.statistics.AbstractClosedFormSmoothUnivariateDistribution

getMean, sampleAsDouble, sampleAsDoubles, sampleInto

Methods inherited from class gov.sandia.cognition.statistics.AbstractDistribution

sample, sample

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface gov.sandia.cognition.statistics.SmoothUnivariateDistribution

getMean, getProbabilityFunction, sampleAsDouble, sampleAsDoubles, sampleInto

Methods inherited from interface gov.sandia.cognition.statistics.UnivariateDistribution

getMaxSupport, getMeanAsDouble, getMinSupport, getVariance

Methods inherited from interface gov.sandia.cognition.statistics.Distribution

sample, sample, sampleInto

Methods inherited from interface gov.sandia.cognition.math.matrix.Vectorizable

clone, convertFromVector, convertToVector

Constructor Detail

CDF

public CDF()

Default constructor.

CDF

public CDF(double degreesOfFreedom)

Creates a new instance of CDF

Parameters:

degreesOfFreedom - Degrees of freedom in the distribution, usually the number of datapoints - 1, DOFs must be greater than zero.

CDF

public CDF(double degreesOfFreedom,
           double mean,
           double precision)

Creates a new instance of PDF

Parameters:

degreesOfFreedom - Degrees of freedom in the distribution, usually the number of datapoints - 1, DOFs must be greater than zero.

mean - Mean, or noncentrality parameter, of the distribution

precision - Precision, inverseRootFinder variance, of the distribution, must be greater than zero.

CDF

public CDF(StudentTDistribution other)

Creates a new instance of CDF

Parameters:

other - The underlying Student t-distribution

Method Detail

evaluate

public java.lang.Double evaluate(java.lang.Double input)

Description copied from interface: Evaluator

Evaluates the function on the given input and returns the output.

Specified by:

evaluate in interface Evaluator<java.lang.Double,java.lang.Double>

Specified by:

evaluate in interface ScalarFunction<java.lang.Double>

Specified by:

evaluate in interface UnivariateScalarFunction

Parameters:

input - The input to evaluate.

Returns:

The output produced by evaluating the input.

evaluateAsDouble

public double evaluateAsDouble(java.lang.Double input)

Description copied from interface: ScalarFunction

Evaluates the scalar function as a double.

Specified by:

evaluateAsDouble in interface ScalarFunction<java.lang.Double>

Specified by:

evaluateAsDouble in interface UnivariateScalarFunction

Parameters:

input - The input value.

Returns:

The scalar output calculated from the given input.

evaluate

public double evaluate(double input)

Description copied from interface: UnivariateScalarFunction

Produces a double output for the given double input

Specified by:

evaluate in interface UnivariateScalarFunction

Parameters:

input - Input to the Evaluator

Returns:

output at the given input

inverse

public java.lang.Double inverse(double p)

Evaluates the Inverse Student-t CDF for the given probability and degrees of freedom

Specified by:

inverse in interface InvertibleCumulativeDistributionFunction<java.lang.Double>

Parameters:

p - Value at which to solve for x such that x=CDF(p)

Returns:

Value of x such that x=CDF(p)

getCDF

public StudentTDistribution.CDF getCDF()

Description copied from interface: UnivariateDistribution

Gets the CDF of a scalar distribution.

Specified by:

getCDF in interface ClosedFormUnivariateDistribution<java.lang.Double>

Specified by:

getCDF in interface SmoothUnivariateDistribution

Specified by:

getCDF in interface UnivariateDistribution<java.lang.Double>

Overrides:

getCDF in class StudentTDistribution

Returns:

CDF of the scalar distribution.

getDerivative

public StudentTDistribution.PDF getDerivative()

Description copied from interface: ClosedFormDifferentiableEvaluator

Gets the closed-form derivative of the function.

Specified by:

getDerivative in interface ClosedFormDifferentiableEvaluator<java.lang.Double,java.lang.Double,java.lang.Double>

Specified by:

getDerivative in interface SmoothCumulativeDistributionFunction

Returns:

Closed-form derivative of the function.

differentiate

public java.lang.Double differentiate(java.lang.Double input)

Description copied from interface: DifferentiableEvaluator

Differentiates the output with respect to the input

Specified by:

differentiate in interface DifferentiableEvaluator<java.lang.Double,java.lang.Double,java.lang.Double>

Parameters:

input - Input about which to compute the derivative

Returns:

Derivative of the output with respect to the given input