de.lmu.ifi.dbs.elki.math.statistics.distribution

Interface Distribution

All Known Implementing Classes:

AbstractDistribution, BetaDistribution, CauchyDistribution, ChiDistribution, ChiSquaredDistribution, ConstantDistribution, ExponentialDistribution, ExponentiallyModifiedGaussianDistribution, GammaDistribution, GeneralizedExtremeValueDistribution, GeneralizedLogisticAlternateDistribution, GeneralizedLogisticDistribution, GeneralizedParetoDistribution, GumbelDistribution, HaltonUniformDistribution, KappaDistribution, LaplaceDistribution, LogGammaAlternateDistribution, LogGammaDistribution, LogisticDistribution, LogLogisticDistribution, LogNormalDistribution, NormalDistribution, PoissonDistribution, RayleighDistribution, SkewGeneralizedNormalDistribution, StudentsTDistribution, UniformDistribution, WaldDistribution, WeibullDistribution

public interface Distribution

Statistical distributions, with their common functions.

Author:

Erich Schubert

Method Summary

Methods

Modifier and Type	Method and Description
double	cdf(double val) Return the cumulative density function at the given value.
double	nextRandom() Generate a new random value
double	pdf(double val) Return the density of an existing value
double	quantile(double val) Quantile aka probit (for normal) aka inverse CDF (invcdf, cdf^-1) function.
String	toString() Describe the distribution

Method Detail

pdf

double pdf(double val)

Return the density of an existing value

Parameters:

val - existing value

Returns:

distribution density

cdf

double cdf(double val)

Return the cumulative density function at the given value.

Parameters:

val - existing value

Returns:

cumulative density

quantile

double quantile(double val)

Quantile aka probit (for normal) aka inverse CDF (invcdf, cdf^-1) function.

Parameters:

val - Quantile to find

Returns:

Quantile position

nextRandom

double nextRandom()

Generate a new random value

Returns:

new random value

toString

String toString()

Describe the distribution

Overrides:

toString in class Object

Returns:

description