# Uses of Interface
org.apache.commons.math3.distribution.RealDistribution

Packages that use RealDistribution
Package
Description
org.apache.commons.math3.distribution
Implementations of common discrete and continuous distributions.
org.apache.commons.math3.ml.neuralnet
Neural networks.
org.apache.commons.math3.random
Random number and random data generators.
org.apache.commons.math3.stat.inference
Classes providing hypothesis testing.

## Uses of RealDistribution in org.apache.commons.math3.distribution

Classes in org.apache.commons.math3.distribution that implement RealDistribution
Modifier and Type
Class
Description
`class`
`AbstractRealDistribution`
Base class for probability distributions on the reals.
`class`
`BetaDistribution`
Implements the Beta distribution.
`class`
`CauchyDistribution`
Implementation of the Cauchy distribution.
`class`
`ChiSquaredDistribution`
Implementation of the chi-squared distribution.
`class`
`ConstantRealDistribution`
Implementation of the constant real distribution.
`class`
`EnumeratedRealDistribution`
Implementation of a real-valued `EnumeratedDistribution`.
`class`
`ExponentialDistribution`
Implementation of the exponential distribution.
`class`
`FDistribution`
Implementation of the F-distribution.
`class`
`GammaDistribution`
Implementation of the Gamma distribution.
`class`
`GumbelDistribution`
This class implements the Gumbel distribution.
`class`
`LaplaceDistribution`
This class implements the Laplace distribution.
`class`
`LevyDistribution`
This class implements the Lévy distribution.
`class`
`LogisticDistribution`
This class implements the Logistic distribution.
`class`
`LogNormalDistribution`
Implementation of the log-normal (gaussian) distribution.
`class`
`NakagamiDistribution`
This class implements the Nakagami distribution.
`class`
`NormalDistribution`
Implementation of the normal (gaussian) distribution.
`class`
`ParetoDistribution`
Implementation of the Pareto distribution.
`class`
`TDistribution`
Implementation of Student's t-distribution.
`class`
`TriangularDistribution`
Implementation of the triangular real distribution.
`class`
`UniformRealDistribution`
Implementation of the uniform real distribution.
`class`
`WeibullDistribution`
Implementation of the Weibull distribution.

## Uses of RealDistribution in org.apache.commons.math3.ml.neuralnet

Methods in org.apache.commons.math3.ml.neuralnet with parameters of type RealDistribution
Modifier and Type
Method
Description
`static FeatureInitializer`
FeatureInitializerFactory.`randomize(RealDistribution random, FeatureInitializer orig)`
Adds some amount of random data to the given initializer.

## Uses of RealDistribution in org.apache.commons.math3.random

Classes in org.apache.commons.math3.random that implement RealDistribution
Modifier and Type
Class
Description
`class`
`EmpiricalDistribution`
Represents an empirical probability distribution -- a probability distribution derived from observed data without making any assumptions about the functional form of the population distribution that the data come from.
Methods in org.apache.commons.math3.random that return RealDistribution
Modifier and Type
Method
Description
`protected RealDistribution`
EmpiricalDistribution.`getKernel(SummaryStatistics bStats)`
The within-bin smoothing kernel.
Methods in org.apache.commons.math3.random with parameters of type RealDistribution
Modifier and Type
Method
Description
`double`
RandomDataImpl.`nextInversionDeviate(RealDistribution distribution)`
Deprecated.
use the distribution's sample() method

## Uses of RealDistribution in org.apache.commons.math3.stat.inference

Methods in org.apache.commons.math3.stat.inference with parameters of type RealDistribution
Modifier and Type
Method
Description
`double`
KolmogorovSmirnovTest.`kolmogorovSmirnovStatistic(RealDistribution distribution, double[] data)`
Computes the one-sample Kolmogorov-Smirnov test statistic, \(D_n=\sup_x |F_n(x)-F(x)|\) where \(F\) is the distribution (cdf) function associated with `distribution`, \(n\) is the length of `data` and \(F_n\) is the empirical distribution that puts mass \(1/n\) at each of the values in `data`.
`static double`
TestUtils.`kolmogorovSmirnovStatistic(RealDistribution dist, double[] data)`
`double`
KolmogorovSmirnovTest.`kolmogorovSmirnovTest(RealDistribution distribution, double[] data)`
Computes the p-value, or observed significance level, of a one-sample Kolmogorov-Smirnov test evaluating the null hypothesis that `data` conforms to `distribution`.
`double`
KolmogorovSmirnovTest.`kolmogorovSmirnovTest(RealDistribution distribution, double[] data, boolean exact)`
Computes the p-value, or observed significance level, of a one-sample Kolmogorov-Smirnov test evaluating the null hypothesis that `data` conforms to `distribution`.
`boolean`
KolmogorovSmirnovTest.`kolmogorovSmirnovTest(RealDistribution distribution, double[] data, double alpha)`
Performs a Kolmogorov-Smirnov test evaluating the null hypothesis that `data` conforms to `distribution`.
`static double`
TestUtils.`kolmogorovSmirnovTest(RealDistribution dist, double[] data)`
`static double`
TestUtils.`kolmogorovSmirnovTest(RealDistribution dist, double[] data, boolean strict)`
`static boolean`
TestUtils.`kolmogorovSmirnovTest(RealDistribution dist, double[] data, double alpha)`