gov.sandia.cognition.statistics.distribution
Class StudentTDistribution.PDF

java.lang.Object
  gov.sandia.cognition.util.AbstractCloneableSerializable
      gov.sandia.cognition.statistics.AbstractDistribution<NumberType>
          gov.sandia.cognition.statistics.AbstractClosedFormUnivariateDistribution<Double>
              gov.sandia.cognition.statistics.AbstractClosedFormSmoothUnivariateDistribution
                  gov.sandia.cognition.statistics.distribution.StudentTDistribution
                      gov.sandia.cognition.statistics.distribution.StudentTDistribution.PDF

All Implemented Interfaces:

Evaluator<Double,Double>, Vectorizable, ScalarFunction<Double>, UnivariateScalarFunction, ClosedFormComputableDistribution<Double>, ClosedFormDistribution<Double>, ClosedFormUnivariateDistribution<Double>, ComputableDistribution<Double>, Distribution<Double>, DistributionWithMean<Double>, EstimableDistribution<Double,StudentTDistribution>, ProbabilityDensityFunction<Double>, ProbabilityFunction<Double>, SmoothUnivariateDistribution, UnivariateDistribution<Double>, UnivariateProbabilityDensityFunction, CloneableSerializable, Serializable, Cloneable

Enclosing class:

StudentTDistribution

public static class StudentTDistribution.PDF
extends StudentTDistribution
implements UnivariateProbabilityDensityFunction

Evaluator that computes the Probability Density 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

StudentTDistribution.PDF()
Default constructor.

StudentTDistribution.PDF(double degreesOfFreedom)
Creates a new instance of PDF

StudentTDistribution.PDF(double degreesOfFreedom, double mean, double precision)
Creates a new instance of PDF

StudentTDistribution.PDF(StudentTDistribution other)
Creates a new instance of PDF

Method Summary

double	evaluate(double input) Produces a double output for the given double input
Double	evaluate(Double input) Evaluates the function on the given input and returns the output.
double	evaluateAsDouble(Double input) Evaluates the scalar function as a double.
StudentTDistribution.PDF	getProbabilityFunction() Gets the distribution function associated with this Distribution, either the PDF or PMF.
double	logEvaluate(double input) Evaluate the natural logarithm of the distribution function.
double	logEvaluate(Double input) Evaluate the natural logarithm of the distribution function.

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

clone, convertFromVector, convertToVector, getCDF, getDegreesOfFreedom, getEstimator, getMaxSupport, getMean, getMinSupport, getPrecision, getVariance, sample, setDegreesOfFreedom, setMean, setPrecision, toString

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

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

getCDF, getMean

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

getMaxSupport, getMinSupport, getVariance

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

sample, sample

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

clone, convertFromVector, convertToVector

Constructor Detail

StudentTDistribution.PDF

public StudentTDistribution.PDF()

Default constructor.

StudentTDistribution.PDF

public StudentTDistribution.PDF(double degreesOfFreedom)

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.

StudentTDistribution.PDF

public StudentTDistribution.PDF(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.

StudentTDistribution.PDF

public StudentTDistribution.PDF(StudentTDistribution other)

Creates a new instance of PDF

Parameters:

other - The underlying Student t-distribution

Method Detail

evaluate

public Double evaluate(Double input)

Description copied from interface: Evaluator

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

Specified by:

evaluate in interface Evaluator<Double,Double>

Parameters:

input - The input to evaluate.

Returns:

The output produced by evaluating the input.

evaluateAsDouble

public double evaluateAsDouble(Double input)

Description copied from interface: ScalarFunction

Evaluates the scalar function as a double.

Specified by:

evaluateAsDouble in interface ScalarFunction<Double>

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

logEvaluate

public double logEvaluate(Double input)

Description copied from interface: ProbabilityFunction

Evaluate the natural logarithm of the distribution function. This is sometimes more efficient than evaluating the distribution function itself, and when evaluating the product of many independent or exchangeable samples.

Specified by:

logEvaluate in interface ProbabilityFunction<Double>

Returns:

Natural logarithm of the distribution function.

logEvaluate

public double logEvaluate(double input)

Description copied from interface: UnivariateProbabilityDensityFunction

Evaluate the natural logarithm of the distribution function. This is sometimes more efficient than evaluating the distribution function itself, and when evaluating the product of many independent or exchangeable samples.

Specified by:

logEvaluate in interface UnivariateProbabilityDensityFunction

Parameters:

input - The input value.

Returns:

The natural logarithm of the distribution function.

getProbabilityFunction

public StudentTDistribution.PDF getProbabilityFunction()

Description copied from interface: ComputableDistribution

Gets the distribution function associated with this Distribution, either the PDF or PMF.

Specified by:

getProbabilityFunction in interface ComputableDistribution<Double>

Specified by:

getProbabilityFunction in interface ProbabilityDensityFunction<Double>

Specified by:

getProbabilityFunction in interface SmoothUnivariateDistribution

Specified by:

getProbabilityFunction in interface UnivariateProbabilityDensityFunction

Overrides:

getProbabilityFunction in class StudentTDistribution

Returns:

Distribution function associated with this Distribution.