Type Members

1.  class Bernoulli extends Rng[Boolean]

   Boolean distribution: generates true with probability p and false with probabilty 1-p.

   Boolean distribution: generates true with probability p and false with probabilty 1-p.

   Exceptions thrown

   IllegalArgumentException if p not in [0, 1]

2.  class Cauchy extends Rng[Double]

   Generates values conforming to a standard Cauchy (or Lorentz) distribution.

   Generates values conforming to a standard Cauchy (or Lorentz) distribution.

   See also

   https://de.wikipedia.org/wiki/Cauchy-Verteilung

3.  class Discrete extends Rng[Int]

   Draws values from a discrete distribution given by an array a of probabilities.

   Draws values from a discrete distribution given by an array a of probabilities.

   More precise, if n=a.length, a value i with i in {0, ..., n-1} is drawn with probability a_i.

   Exceptions thrown

   IllegalArgumentException if an element of a is negative or the sum over all elements of a is not 1 (within machine accuracy)

4.  class DoubleUniform extends Rng[Double]

   Generates uniformly distributed Double values in [a, b).

   Generates uniformly distributed Double values in [a, b).

   Exceptions thrown

   IllegalArgumentException if not a less than b

5.  class Exponential extends Rng[Double]

   Generates values conforming to an exponential distribution with rate parameter λ.

   Generates values conforming to an exponential distribution with rate parameter λ.

   Exceptions thrown

   IllegalArgumentException if lambda less than or equal to 0

   See also

   https://de.wikipedia.org/wiki/Exponentialverteilung

6.  class Gaussian extends Rng[Double]

   Generates values conforming to a normal (or Gaussian) distribution with given mean and standard deviation

7.  class Geometric extends Rng[Int]

   Generates values conforming to a geometric distribution with mean 1/p.

   Generates values conforming to a geometric distribution with mean 1/p.

   Exceptions thrown

   IllegalArgumentException if p not in [0, 1]

8.  class IntUniform extends Rng[Int]

   Generates uniformly distributed Int values in {a, b-1}.

   Generates uniformly distributed Int values in {a, b-1}.

   Exceptions thrown

   IllegalArgumentException if not a less than b

9.  class Lomax extends Rng[Double]

   Generates values conforming to a Lomax (shifted Pareto) distribution with shape parameter α and scala parameter λ.

   Generates values conforming to a Lomax (shifted Pareto) distribution with shape parameter α and scala parameter λ.

   Exceptions thrown

   IllegalArgumentException if alpha or xmin not positive

   See also

   https://en.wikipedia.org/wiki/Lomax_distribution

10.  class LongUniform extends Rng[Long]

    Generates uniformly distributed Long values in {a, b-1}.

    Generates uniformly distributed Long values in {a, b-1}.

    Exceptions thrown

    IllegalArgumentException if not a less than b

11.  class Pareto extends Rng[Double]

    Generates values conforming to a Pareto distribution with shape parameter α and minimum value x_min.

    Generates values conforming to a Pareto distribution with shape parameter α and minimum value x_min.

    Exceptions thrown

    IllegalArgumentException if alpha or xmin not positive

    See also

    https://en.wikipedia.org/wiki/Pareto_distribution

12.  class Poisson extends Rng[Int]

    Generates values conforming to a Poisson distribution P_λ(k) = (λ^k/k!)e^-λ.

    Generates values conforming to a Poisson distribution P_λ(k) = (λ^k/k!)e^-λ.

    The Poisson distribution yields the probability of the occurence of k events within a certain period of time, assuming that λ denotes the average occurence of events during that period and privided that events are independent of the time since the last event.

    It turns out that λ is both the expected value and the variance of the distribution.

    Exceptions thrown

    IllegalArgumentException if lambda not positive or not finite

    See also

    https://de.wikipedia.org/wiki/Poisson-Verteilung

13.  trait Rng[A] extends AnyRef

    Defines a basic random-number-generator trait to be used by various concrete generators implementing specific probability distributions.