Class ExponentialDistributionImpl

All Implemented Interfaces:
Serializable, ContinuousDistribution, Distribution, ExponentialDistribution, HasDensity<Double>

public class ExponentialDistributionImpl extends AbstractContinuousDistribution implements ExponentialDistribution, Serializable
The default implementation of ExponentialDistribution.
Version:
$Revision: 1055914 $ $Date: 2011-01-06 16:34:34 +0100 (jeu. 06 janv. 2011) $
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

    • ExponentialDistributionImpl

      public ExponentialDistributionImpl(double mean)
      Create a exponential distribution with the given mean.
      Parameters:
      mean - mean of this distribution.
    • ExponentialDistributionImpl

      public ExponentialDistributionImpl(double mean, double inverseCumAccuracy)
      Create a exponential distribution with the given mean.
      Parameters:
      mean - mean of this distribution.
      inverseCumAccuracy - the maximum absolute error in inverse cumulative probability estimates (defaults to DEFAULT_INVERSE_ABSOLUTE_ACCURACY)
      Since:
      2.1
  • Method Details

    • setMean

      @Deprecated public void setMean(double mean)
      Deprecated.
      as of 2.1 (class will become immutable in 3.0)
      Modify the mean.
      Specified by:
      setMean in interface ExponentialDistribution
      Parameters:
      mean - the new mean.
      Throws:
      IllegalArgumentException - if mean is not positive.
    • getMean

      public double getMean()
      Access the mean.
      Specified by:
      getMean in interface ExponentialDistribution
      Returns:
      the mean.
    • density

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

      public double density(double x)
      Return 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.
      Since:
      2.1
    • 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:
      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 invalid input: '<' 0 or p > 1.
    • sample

      public double sample() throws MathException
      Generates a random value sampled from this distribution.

      Algorithm Description: Uses the Inversion Method to generate exponentially distributed random values from uniform deviates.

      Overrides:
      sample in class AbstractContinuousDistribution
      Returns:
      random value
      Throws:
      MathException - if an error occurs generating the random value
      Since:
      2.2
    • getDomainLowerBound

      protected double getDomainLowerBound(double p)
      Access the domain value lower bound, based on p, used to bracket a CDF root.
      Specified by:
      getDomainLowerBound in class AbstractContinuousDistribution
      Parameters:
      p - the desired probability for the critical value
      Returns:
      domain value lower bound, i.e. P(X < lower bound) < p
    • getDomainUpperBound

      protected double getDomainUpperBound(double p)
      Access the domain value upper bound, based on p, used to bracket a CDF root.
      Specified by:
      getDomainUpperBound in class AbstractContinuousDistribution
      Parameters:
      p - the desired probability for the critical value
      Returns:
      domain value upper bound, i.e. P(X < upper bound) > p
    • getInitialDomain

      protected double getInitialDomain(double p)
      Access the initial domain value, based on p, used to bracket a CDF root.
      Specified by:
      getInitialDomain in class AbstractContinuousDistribution
      Parameters:
      p - the desired probability for the critical value
      Returns:
      initial domain value
    • getSolverAbsoluteAccuracy

      protected double getSolverAbsoluteAccuracy()
      Return the absolute accuracy setting of the solver used to estimate inverse cumulative probabilities.
      Overrides:
      getSolverAbsoluteAccuracy in class AbstractContinuousDistribution
      Returns:
      the solver absolute accuracy
      Since:
      2.1
    • getSupportLowerBound

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

      public double getNumericalMean()
      Returns the mean of the distribution. For mean parameter k, the mean is k
      Returns:
      the mean
      Since:
      2.2
    • getNumericalVariance

      public double getNumericalVariance()
      Returns the variance of the distribution. For mean parameter k, the variance is k^2
      Returns:
      the variance
      Since:
      2.2