Class LogNormal

All Implemented Interfaces:
Comparable<RandomNumber>, DoubleSupplier, Supplier<Double>, BasicFunction, NullaryFunction<Double>, PrimitiveFunction.Nullary, ContinuousDistribution, Distribution, AccessScalar<Double>, ComparableNumber<RandomNumber>, NumberDefinition

public class LogNormal extends AbstractContinuous
A continuous distribution in which the logarithm of a variable has a normal distribution. A log normal distribution results if the variable is the product of a large number of independent, identically-distributed variables in the same way that a normal distribution results if the variable is the sum of a large number of independent, identically-distributed variables.
  • Field Details

    • myNormal

      private final Normal myNormal
  • Constructor Details

    • LogNormal

      public LogNormal()
    • LogNormal

      public LogNormal(double location, double scale)
      The location and scale parameters are the mean and standard deviation of the variable's logarithm (by definition, the variable's logarithm is normally distributed).
  • Method Details

    • estimate

      public static LogNormal estimate(Access1D<?> rawSamples)
    • make

      public static LogNormal make(double mean, double variance)
    • 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()
    • getGeometricMean

      public double getGeometricMean()
      The geometric mean is also the median
    • getGeometricStandardDeviation

      public double getGeometricStandardDeviation()
    • 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:
    • setSeed

      public void setSeed(long seed)
      Overrides:
      setSeed in class RandomNumber
    • generate

      protected double generate()
      Overrides:
      generate in class AbstractContinuous