gov.sandia.cognition.statistics.distribution

Class BetaDistribution.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.BetaDistribution

gov.sandia.cognition.statistics.distribution.BetaDistribution.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,BetaDistribution>, SmoothCumulativeDistributionFunction, SmoothUnivariateDistribution, UnivariateDistribution<java.lang.Double>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

BetaDistribution

public static class BetaDistribution.CDF
extends BetaDistribution
implements SmoothCumulativeDistributionFunction

CDF of the Beta-family distribution

See Also:

Serialized Form

Nested Class Summary

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

BetaDistribution.CDF, BetaDistribution.MomentMatchingEstimator, BetaDistribution.PDF, BetaDistribution.WeightedMomentMatchingEstimator

Field Summary

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

DEFAULT_ALPHA, DEFAULT_BETA

Constructor Summary

Constructors

Constructor and Description
CDF() Default constructor.
CDF(BetaDistribution other) Copy constructor
CDF(double alpha, double beta) Creates a new 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.
static double	evaluate(double x, double alpha, double beta) Evaluate the Beta-distribution CDF for Beta(x;alpha,beta)
double	evaluateAsDouble(java.lang.Double input) Evaluates the scalar function as a double.
BetaDistribution.CDF	getCDF() Gets the CDF of a scalar distribution.
BetaDistribution.PDF	getDerivative() Gets the closed-form derivative of the function.

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

clone, convertFromVector, convertToVector, getAlpha, getBeta, getEstimator, getMaxSupport, getMeanAsDouble, getMinSupport, getProbabilityFunction, getVariance, sampleAsDouble, sampleInto, setAlpha, setBeta

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

getMean, 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, toString, 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 alpha,
           double beta)

Creates a new CDF

Parameters:

alpha - Alpha shape parameter, must be greater than 0 (typically greater than 1)

beta - Beta shape parameter, must be greater than 0 (typically greater than 1)

CDF

public CDF(BetaDistribution other)

Copy constructor

Parameters:

other - Underlying Beta 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

evaluate

public static double evaluate(double x,
                              double alpha,
                              double beta)

Evaluate the Beta-distribution CDF for Beta(x;alpha,beta)

Parameters:

x - Input to the beta CDF, must be on the interval [0,1]

alpha - Alpha shape parameter, must be greater than 0 (typically greater than 1)

beta - Beta shape parameter, must be greater than 0 (typically greater than 1)

Returns:

Beta(x;alpha,beta)

getCDF

public BetaDistribution.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 BetaDistribution

Returns:

CDF of the scalar distribution.

getDerivative

public BetaDistribution.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