## 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 and 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` | `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` | `LevyDistribution` This class implements the Lévy distribution.
    `class` | `LogNormalDistribution` Implementation of the log-normal (gaussian) 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 and 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 and 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 and 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 and 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 and 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`.
    `static double` | TestUtils.`kolmogorovSmirnovTest(RealDistribution dist, double[] data)`
    `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`.
    `static double` | TestUtils.`kolmogorovSmirnovTest(RealDistribution dist, double[] data, boolean strict)`
    `boolean` | KolmogorovSmirnovTest.`kolmogorovSmirnovTest(RealDistribution distribution, double[] data, double alpha)` Performs a Kolmogorov-Smirnov test evaluating the null hypothesis that `data` conforms to `distribution`.
    `static boolean` | TestUtils.`kolmogorovSmirnovTest(RealDistribution dist, double[] data, double alpha)`

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