Package org.apache.commons.statistics.distribution

Interface ContinuousDistribution

All Known Subinterfaces:
RealDistribution
All Known Implementing Classes:
AbstractRealDistribution, BetaDistribution, CauchyDistribution, ChiSquaredDistribution, ConstantContinuousDistribution, EmpiricalDistribution, EnumeratedRealDistribution, ExponentialDistribution, FDistribution, GammaDistribution, GumbelDistribution, LaplaceDistribution, LevyDistribution, LogisticDistribution, LogNormalDistribution, NakagamiDistribution, NormalDistribution, ParetoDistribution, TDistribution, TriangularDistribution, UniformContinuousDistribution, WeibullDistribution
public interface ContinuousDistribution
Base interface for distributions on the reals.

  • Nested Class Summary

    Nested Classes 
    Modifier and Type Interface Description
    static interface  ContinuousDistribution.Sampler
    Sampling functionality.

  • Method Summary

    Modifier and Type Method Description
    ContinuousDistribution.Sampler createSampler​(UniformRandomProvider rng)
    Creates a sampler.
    double cumulativeProbability​(double x)
    For a random variable X whose values are distributed according to this distribution, this method returns P(X <= x).
    double density​(double x)
    Returns the probability density function (PDF) of this distribution evaluated at the specified point x.
    double getMean()
    Gets the mean of this distribution.
    double getSupportLowerBound()
    Gets the lower bound of the support.
    double getSupportUpperBound()
    Gets the upper bound of the support.
    double getVariance()
    Gets the variance of this distribution.
    double inverseCumulativeProbability​(double p)
    Computes the quantile function of this distribution.
    boolean isSupportConnected()
    Indicates whether the support is connected, i.e.
    default double logDensity​(double x)
    Returns the natural logarithm of the probability density function (PDF) of this distribution evaluated at the specified point x.
    default double probability​(double x)
    For a random variable X whose values are distributed according to this distribution, this method returns P(X = x).
    default double probability​(double x0, double x1)
    For a random variable X whose values are distributed according to this distribution, this method returns P(x0 < X <= x1).

  • Method Details

    • probability

      default double probability​(double x)
      For a random variable X whose values are distributed according to this distribution, this method returns P(X = x). In other words, this method represents the probability mass function (PMF) for the distribution.
      Parameters:
      x - Point at which the PMF is evaluated.
      Returns:
      the value of the probability mass function at point x.

    • probability

      default double probability​(double x0, double x1)
      For a random variable X whose values are distributed according to this distribution, this method returns P(x0 < X <= x1). The default implementation uses the identity P(x0 < X <= x1) = P(X <= x1) - P(X <= x0)
      Parameters:
      x0 - Lower bound (exclusive).
      x1 - Upper bound (inclusive).
      Returns:
      the probability that a random variable with this distribution takes a value between x0 and x1, excluding the lower and including the upper endpoint.
      Throws:
      java.lang.IllegalArgumentException - if x0 > x1.

    • density

      double density​(double x)
      Returns the probability density function (PDF) of this distribution evaluated at the specified point x. In general, the PDF is the derivative of the CDF. If the derivative does not exist at x, then an appropriate replacement should be returned, e.g. Double.POSITIVE_INFINITY, Double.NaN, or the limit inferior or limit superior of the difference quotient.
      Parameters:
      x - Point at which the PDF is evaluated.
      Returns:
      the value of the probability density function at x.

    • logDensity

      default double logDensity​(double x)
      Returns the natural logarithm of the probability density function (PDF) of this distribution evaluated at the specified point x.
      Parameters:
      x - Point at which the PDF is evaluated.
      Returns:
      the logarithm of the value of the probability density function at x.

    • cumulativeProbability

      double cumulativeProbability​(double x)
      For a random variable X whose values are distributed according to this distribution, this method returns P(X <= x). In other words, this method represents the (cumulative) distribution function (CDF) for this distribution.
      Parameters:
      x - Point at which the CDF is evaluated.
      Returns:
      the probability that a random variable with this distribution takes a value less than or equal to x.

    • inverseCumulativeProbability

      double inverseCumulativeProbability​(double p)
      Computes the quantile function of this distribution. For a random variable X distributed according to this distribution, the returned value is
      • inf{x in R | P(X<=x) >= p} for 0 < p <= 1,
      • inf{x in R | P(X<=x) > 0} for p = 0.
      Parameters:
      p - Cumulative probability.
      Returns:
      the smallest p-quantile of this distribution (largest 0-quantile for p = 0).
      Throws:
      java.lang.IllegalArgumentException - if p < 0 or p > 1.

    • getMean

      double getMean()
      Gets the mean of this distribution.
      Returns:
      the mean, or Double.NaN if it is not defined.

    • getVariance

      double getVariance()
      Gets the variance of this distribution.
      Returns:
      the variance, or Double.NaN if it is not defined.

    • getSupportLowerBound

      double getSupportLowerBound()
      Gets the lower bound of the support. It must return the same value as inverseCumulativeProbability(0), i.e. inf {x in R | P(X <= x) > 0}.
      Returns:
      the lower bound of the support.

    • getSupportUpperBound

      double getSupportUpperBound()
      Gets the upper bound of the support. It must return the same value as inverseCumulativeProbability(1), i.e. inf {x in R | P(X <= x) = 1}.
      Returns:
      the upper bound of the support.

    • isSupportConnected

      boolean isSupportConnected()
      Indicates whether the support is connected, i.e. whether all values between the lower and upper bound of the support are included in the support.
      Returns:
      whether the support is connected.

    • createSampler

      ContinuousDistribution.Sampler createSampler​(UniformRandomProvider rng)
      Creates a sampler.
      Parameters:
      rng - Generator of uniformly distributed numbers.
      Returns:
      a sampler that produces random numbers according this distribution.