Class TDistribution

java.lang.Object
org.ojalgo.random.RandomNumber
org.ojalgo.random.AbstractContinuous
org.ojalgo.random.TDistribution
All Implemented Interfaces:
Comparable<RandomNumber>, DoubleSupplier, Supplier<Double>, BasicFunction, NullaryFunction<Double>, PrimitiveFunction.Nullary, ContinuousDistribution, Distribution, AccessScalar<Double>, ComparableNumber<RandomNumber>, NumberDefinition
Direct Known Subclasses:
TDistribution.Degree1, TDistribution.Degree2, TDistribution.Degree3, TDistribution.Degree4, TDistribution.Degree5, TDistribution.DegreeInfinity
public class TDistribution extends AbstractContinuous

  • Field Details

    • _0_0001

      private static final double _0_0001
      See Also:

    • NORMAL

      private static final Normal NORMAL

    • myConstant

      private final double myConstant
      The density and distribution functions share a common constant factor

    • myDegreesOfFreedom

      private final double myDegreesOfFreedom

  • Constructor Details

    • TDistribution

      TDistribution(double degreesOfFreedom)

  • Method Details

    • of

      public static TDistribution of(int degreesOfFreedom)

    • ofInfinity

      public static TDistribution ofInfinity()

    • getDensity

      public double getDensity(double value)
      Description copied from interface: ContinuousDistribution
      In probability theory, a probability density function (pdf), or density of a continuous random variable is a function that describes the relative likelihood for this random variable to occur at a given point. The probability for the random variable to fall within a particular region is given by the integral of this variable's density over the region. The probability density function is nonnegative everywhere, and its integral over the entire space is equal to one. WikipediA
      Parameters:
      value - x
      Returns:
      P(x)

    • getDistribution

      public double getDistribution(double value)
      Description copied from interface: ContinuousDistribution
      In probability theory and statistics, the cumulative distribution function (CDF), or just distribution function, describes the probability that a real-valued random variable X with a given probability distribution will be found at a value less than or equal to x. Intuitively, it is the "area so far" function of the probability distribution. Cumulative distribution functions are also used to specify the distribution of multivariate random variables. WikipediA
      Parameters:
      value - x
      Returns:
      P(≤x)

    • getExpected

      public double getExpected()

    • getQuantile

      public double getQuantile(double probability)
      Description copied from interface: ContinuousDistribution
      The quantile function, for any distribution, is defined for real variables between zero and one and is mathematically the inverse of the cumulative distribution function. WikipediA The input probability absolutely has to be [0.0, 1.0], but values close to 0.0 and 1.0 may be problematic
      Parameters:
      probability - P(<=x)
      Returns:
      x

    • getVariance

      public double getVariance()
      Description copied from class: RandomNumber
      Subclasses must override either getStandardDeviation() or getVariance()!
      Specified by:
      getVariance in interface Distribution
      Overrides:
      getVariance in class RandomNumber
      See Also:

    • approximateQuantile

      private double approximateQuantile(double probability)