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case class Normal(mu: Double = 0.0, sigma2: Double = 1.0, stream: Int = 0) extends Variate with Product with Serializable

This class generates Normal (Gaussian) random variates. This continuous RV models normally distributed data (bell curve). When summed, most distributions tend to Normal (Central Limit Theorem).

mu

the mean

sigma2

the variance (sigma squared)

stream

the random number stream

See also

http://www.math.uah.edu/stat/special/Normal.html

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Serializable, Serializable, Product, Equals, Variate, Error, AnyRef, Any
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Instance Constructors

  1. new Normal(mu: Double = 0.0, sigma2: Double = 1.0, stream: Int = 0)

    mu

    the mean

    sigma2

    the variance (sigma squared)

    stream

    the random number stream

Value Members

  1. def discrete: Boolean

    Determine whether the distribution is discrete or continuous.

    Determine whether the distribution is discrete or continuous.

    Definition Classes
    Variate
  2. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  3. def gen: Double

    Determine the next random number for the particular distribution.

    Determine the next random number for the particular distribution.

    Definition Classes
    Normal → Variate
  4. def gen0: Double
  5. def gen1(z: Double): Double

    Determine the next random number for the particular distribution.

    Determine the next random number for the particular distribution. This version allows one parameter.

    z

    the limit parameter

    Definition Classes
    Normal → Variate
  6. def igen: Int

    Determine the next random integer for the particular distribution.

    Determine the next random integer for the particular distribution. It is only valid for discrete random variates.

    Definition Classes
    Variate
  7. def igen1(z: Double): Int

    Determine the next random integer for the particular distribution.

    Determine the next random integer for the particular distribution. It is only valid for discrete random variates. This version allows one parameter.

    z

    the limit parameter

    Definition Classes
    Variate
  8. val mean: Double

    Precompute the mean for the particular distribution.

    Precompute the mean for the particular distribution.

    Definition Classes
    Normal → Variate
  9. val mu: Double
  10. def pf(z: Double): Double

    Compute the probability function (pf): Either (a) the probability density function (pdf) for continuous RV's or (b) the probability mass function (pmf) for discrete RV's.

    Compute the probability function (pf): Either (a) the probability density function (pdf) for continuous RV's or (b) the probability mass function (pmf) for discrete RV's.

    z

    the mass point whose probability density/mass is sought

    Definition Classes
    Normal → Variate
  11. def pmf(k: Int = 0): Array[Double]

    Return the entire probability mass function (pmf) for finite discrete RV's.

    Return the entire probability mass function (pmf) for finite discrete RV's.

    k

    number of objects of the first type

    Definition Classes
    Variate
  12. def sgen: String

    Determine the next random string for the particular distribution.

    Determine the next random string for the particular distribution. For better random strings, overide this method.

    Definition Classes
    Variate
  13. def sgen1(z: Double): String

    Determine the next random string for the particular distribution.

    Determine the next random string for the particular distribution. For better random strings, overide this method. This version allows one parameter.

    z

    the limit parameter

    Definition Classes
    Variate
  14. val sigma2: Double
  15. val stream: Int