Package org.apache.commons.math3.distribution

Class AbstractRealDistribution

java.lang.Object
org.apache.commons.math3.distribution.AbstractRealDistribution
All Implemented Interfaces:
Serializable, RealDistribution
Direct Known Subclasses:
BetaDistribution, CauchyDistribution, ChiSquaredDistribution, ConstantRealDistribution, EmpiricalDistribution, EnumeratedRealDistribution, ExponentialDistribution, FDistribution, GammaDistribution, GumbelDistribution, LaplaceDistribution, LevyDistribution, LogisticDistribution, LogNormalDistribution, NakagamiDistribution, NormalDistribution, ParetoDistribution, TDistribution, TriangularDistribution, UniformRealDistribution, WeibullDistribution
public abstract class AbstractRealDistribution extends Object implements RealDistribution, Serializable
Base class for probability distributions on the reals. Default implementations are provided for some of the methods that do not vary from distribution to distribution.
Since:
3.0
See Also:

  • Field Details

    • SOLVER_DEFAULT_ABSOLUTE_ACCURACY

      public static final double SOLVER_DEFAULT_ABSOLUTE_ACCURACY
      Default accuracy.
      See Also:

    • randomData

      @Deprecated protected RandomDataImpl randomData
      Deprecated.
      As of 3.1, to be removed in 4.0. Please use the random instance variable instead.
      RandomData instance used to generate samples from the distribution.

    • random

      protected final RandomGenerator random
      RNG instance used to generate samples from the distribution.
      Since:
      3.1

  • Constructor Details

  • Method Details

    • cumulativeProbability

      @Deprecated public double cumulativeProbability(double x0, double x1) throws NumberIsTooLargeException
      Deprecated.
      As of 3.1 (to be removed in 4.0). Please use probability(double,double) instead.
      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)

      Specified by:
      cumulativeProbability in interface RealDistribution
      Parameters:
      x0 - the exclusive lower bound
      x1 - the inclusive upper bound
      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:
      NumberIsTooLargeException - if x0 > x1

    • probability

      public 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).
      Parameters:
      x0 - Lower bound (excluded).
      x1 - Upper bound (included).
      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:
      NumberIsTooLargeException - if x0 > x1. The default implementation uses the identity P(x0 < X <= x1) = P(X <= x1) - P(X <= x0)
      Since:
      3.1

    • inverseCumulativeProbability

      public double inverseCumulativeProbability(double p) throws OutOfRangeException
      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(Xinvalid input: '<'=x) >= p} for 0 < p <= 1,
      • inf{x in R | P(Xinvalid input: '<'=x) > 0} for p = 0.
      The default implementation returns
      Specified by:
      inverseCumulativeProbability in interface RealDistribution
      Parameters:
      p - the cumulative probability
      Returns:
      the smallest p-quantile of this distribution (largest 0-quantile for p = 0)
      Throws:
      OutOfRangeException - if p < 0 or p > 1

    • getSolverAbsoluteAccuracy

      protected double getSolverAbsoluteAccuracy()
      Returns the solver absolute accuracy for inverse cumulative computation. You can override this method in order to use a Brent solver with an absolute accuracy different from the default.
      Returns:
      the maximum absolute error in inverse cumulative probability estimates

    • reseedRandomGenerator

      public void reseedRandomGenerator(long seed)
      Reseed the random generator used to generate samples.
      Specified by:
      reseedRandomGenerator in interface RealDistribution
      Parameters:
      seed - the new seed

    • sample

      public double sample()
      Generate a random value sampled from this distribution. The default implementation uses the inversion method.
      Specified by:
      sample in interface RealDistribution
      Returns:
      a random value.

    • sample

      public double[] sample(int sampleSize)
      Generate a random sample from the distribution. The default implementation generates the sample by calling sample() in a loop.
      Specified by:
      sample in interface RealDistribution
      Parameters:
      sampleSize - the number of random values to generate
      Returns:
      an array representing the random sample

    • probability

      public 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.
      Specified by:
      probability in interface RealDistribution
      Parameters:
      x - the point at which the PMF is evaluated
      Returns:
      zero.
      Since:
      3.1

    • logDensity

      public double logDensity(double x)
      Returns the natural logarithm of 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. Note that due to the floating point precision and under/overflow issues, this method will for some distributions be more precise and faster than computing the logarithm of RealDistribution.density(double). The default implementation simply computes the logarithm of density(x).
      Parameters:
      x - the point at which the PDF is evaluated
      Returns:
      the logarithm of the value of the probability density function at point x