Package org.hipparchus.distribution.continuous

Class ChiSquaredDistribution

java.lang.Object
org.hipparchus.distribution.continuous.AbstractRealDistribution
org.hipparchus.distribution.continuous.ChiSquaredDistribution
All Implemented Interfaces:
Serializable, RealDistribution
public class ChiSquaredDistribution extends AbstractRealDistribution
Implementation of the chi-squared distribution.
See Also:

  • Constructor Details

    • ChiSquaredDistribution

      public ChiSquaredDistribution(double degreesOfFreedom)
      Create a Chi-Squared distribution with the given degrees of freedom.
      Parameters:
      degreesOfFreedom - Degrees of freedom.

    • ChiSquaredDistribution

      public ChiSquaredDistribution(double degreesOfFreedom, double inverseCumAccuracy)
      Create a Chi-Squared distribution with the given degrees of freedom and inverse cumulative probability accuracy.
      Parameters:
      degreesOfFreedom - Degrees of freedom.
      inverseCumAccuracy - the maximum absolute error in inverse cumulative probability estimates (defaults to AbstractRealDistribution.DEFAULT_SOLVER_ABSOLUTE_ACCURACY).

  • Method Details

    • getDegreesOfFreedom

      public double getDegreesOfFreedom()
      Access the number of degrees of freedom.
      Returns:
      the degrees of freedom.

    • density

      public 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 - the point at which the PDF is evaluated
      Returns:
      the value of the probability density function at point x

    • 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).

      Specified by:
      logDensity in interface RealDistribution
      Overrides:
      logDensity in class AbstractRealDistribution
      Parameters:
      x - the point at which the PDF is evaluated
      Returns:
      the logarithm of the value of the probability density function at point x

    • cumulativeProbability

      public 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 - the 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

    • getNumericalMean

      public double getNumericalMean()
      Use this method to get the numerical value of the mean of this distribution. For k degrees of freedom, the mean is k.
      Returns:
      the mean or Double.NaN if it is not defined

    • getNumericalVariance

      public double getNumericalVariance()
      Use this method to get the numerical value of the variance of this distribution.
      Returns:
      2 * k, where k is the number of degrees of freedom.

    • getSupportLowerBound

      public double getSupportLowerBound()
      Access the lower bound of the support. This method must return the same value as inverseCumulativeProbability(0). In other words, this method must return

      inf {x in R | P(X <= x) > 0}.

      The lower bound of the support is always 0 no matter the degrees of freedom.
      Returns:
      zero.

    • getSupportUpperBound

      public double getSupportUpperBound()
      Access the upper bound of the support. This method must return the same value as inverseCumulativeProbability(1). In other words, this method must return

      inf {x in R | P(X <= x) = 1}.

      The upper bound of the support is always positive infinity no matter the degrees of freedom.
      Returns:
      Double.POSITIVE_INFINITY.

    • isSupportConnected

      public boolean isSupportConnected()
      Use this method to get information about whether the support is connected, i.e. whether all values between the lower and upper bound of the support are included in the support. The support of this distribution is connected.
      Returns:
      true