Package org.apache.commons.math.distribution

Class GammaDistributionImpl

java.lang.Object
org.apache.commons.math.distribution.AbstractDistribution
org.apache.commons.math.distribution.AbstractContinuousDistribution
org.apache.commons.math.distribution.GammaDistributionImpl
All Implemented Interfaces:
Serializable, ContinuousDistribution, Distribution, GammaDistribution, HasDensity<Double>
public class GammaDistributionImpl extends AbstractContinuousDistribution implements GammaDistribution, Serializable
The default implementation of GammaDistribution.
See Also:

  • Field Details

    • DEFAULT_INVERSE_ABSOLUTE_ACCURACY

      public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY
      Default inverse cumulative probability accuracy
      Since:
      2.1
      See Also:

  • Constructor Details

    • GammaDistributionImpl

      public GammaDistributionImpl(double alpha, double beta)
      Create a new gamma distribution with the given alpha and beta values.
      Parameters:
      alpha - the shape parameter.
      beta - the scale parameter.

    • GammaDistributionImpl

      public GammaDistributionImpl(double alpha, double beta, double inverseCumAccuracy)
      Create a new gamma distribution with the given alpha and beta values.
      Parameters:
      alpha - the shape parameter.
      beta - the scale parameter.
      inverseCumAccuracy - the maximum absolute error in inverse cumulative probability estimates (defaults to DEFAULT_INVERSE_ABSOLUTE_ACCURACY)
      Since:
      2.1

  • Method Details

    • cumulativeProbability

      public double cumulativeProbability(double x) throws MathException
      For this distribution, X, this method returns P(X < x). The implementation of this method is based on:
      • Chi-Squared Distribution, equation (9).
      • Casella, G., invalid input: '&' Berger, R. (1990). Statistical Inference. Belmont, CA: Duxbury Press.
      Specified by:
      cumulativeProbability in interface Distribution
      Parameters:
      x - the value at which the CDF is evaluated.
      Returns:
      CDF for this distribution.
      Throws:
      MathException - if the cumulative probability can not be computed due to convergence or other numerical errors.

    • inverseCumulativeProbability

      public double inverseCumulativeProbability(double p) throws MathException
      For this distribution, X, this method returns the critical point x, such that P(X < x) = p.

      Returns 0 for p=0 and Double.POSITIVE_INFINITY for p=1.

      Specified by:
      inverseCumulativeProbability in interface ContinuousDistribution
      Overrides:
      inverseCumulativeProbability in class AbstractContinuousDistribution
      Parameters:
      p - the desired probability
      Returns:
      x, such that P(X < x) = p
      Throws:
      MathException - if the inverse cumulative probability can not be computed due to convergence or other numerical errors.
      IllegalArgumentException - if p is not a valid probability.

    • setAlpha

      @Deprecated public void setAlpha(double alpha)
      Deprecated.
      as of 2.1 (class will become immutable in 3.0)
      Modify the shape parameter, alpha.
      Specified by:
      setAlpha in interface GammaDistribution
      Parameters:
      alpha - the new shape parameter.
      Throws:
      IllegalArgumentException - if alpha is not positive.

    • getAlpha

      public double getAlpha()
      Access the shape parameter, alpha
      Specified by:
      getAlpha in interface GammaDistribution
      Returns:
      alpha.

    • setBeta

      @Deprecated public void setBeta(double newBeta)
      Deprecated.
      as of 2.1 (class will become immutable in 3.0)
      Modify the scale parameter, beta.
      Specified by:
      setBeta in interface GammaDistribution
      Parameters:
      newBeta - the new scale parameter.
      Throws:
      IllegalArgumentException - if newBeta is not positive.

    • getBeta

      public double getBeta()
      Access the scale parameter, beta
      Specified by:
      getBeta in interface GammaDistribution
      Returns:
      beta.

    • density

      public double density(double x)
      Returns the probability density for a particular point.
      Overrides:
      density in class AbstractContinuousDistribution
      Parameters:
      x - The point at which the density should be computed.
      Returns:
      The pdf at point x.

    • density

      @Deprecated public double density(Double x)
      Deprecated.
      Return the probability density for a particular point.
      Specified by:
      density in interface GammaDistribution
      Specified by:
      density in interface HasDensity<Double>
      Parameters:
      x - The point at which the density should be computed.
      Returns:
      The pdf at point x.

    • getSupportLowerBound

      public double getSupportLowerBound()
      Returns the upper bound of the support for the distribution. The lower bound of the support is always 0, regardless of the parameters.
      Returns:
      lower bound of the support (always 0)
      Since:
      2.2

    • getSupportUpperBound

      public double getSupportUpperBound()
      Returns the upper bound of the support for the distribution. The upper bound of the support is always positive infinity, regardless of the parameters.
      Returns:
      upper bound of the support (always Double.POSITIVE_INFINITY)
      Since:
      2.2

    • getNumericalMean

      public double getNumericalMean()
      Returns the mean. For shape parameter alpha and scale parameter beta, the mean is alpha * beta
      Returns:
      the mean
      Since:
      2.2

    • getNumericalVariance

      public double getNumericalVariance()
      Returns the variance. For shape parameter alpha and scale parameter beta, the variance is alpha * beta^2
      Returns:
      the variance
      Since:
      2.2