Packages

c

scalation.random

NHPoissonProcess

case class NHPoissonProcess(lambda: VectorD, dt: Double = 1.0, stream: Int = 0) extends TimeVariate with Product with Serializable

This class generates arrival times according to a NHPoissonProcess, an Non-Homogeneous Process Process (NHPP), where the arrival rate function 'lambda(t)' is piece-wise constant. Rates are constant over basic time intervals of length 'dt'.

lambda

the vector of arrival rates

dt

the length the basic time intervals

stream

the random number stream

See also

http://en.wikipedia.org/wiki/Poisson_process#Non-homogeneous

Linear Supertypes
Serializable, Product, Equals, TimeVariate, Variate, Error, AnyRef, Any
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Inherited
  1. NHPoissonProcess
  2. Serializable
  3. Product
  4. Equals
  5. TimeVariate
  6. Variate
  7. Error
  8. AnyRef
  9. Any
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Visibility
  1. Public
  2. Protected

Instance Constructors

  1. new NHPoissonProcess(lambda: VectorD, dt: Double = 1.0, stream: Int = 0)

    lambda

    the vector of arrival rates

    dt

    the length the basic time intervals

    stream

    the random number stream

Value Members

  1. def count(a: Double, b: Double): Int
    Definition Classes
    TimeVariate
  2. def count(tt: Double): Int

    Compute the mean as a function of time.

    Compute the mean as a function of time.

    tt

    the time point for computing the mean

    Definition Classes
    TimeVariate
  3. def discrete: Boolean

    Determine whether the distribution is discrete or continuous.

    Determine whether the distribution is discrete or continuous.

    Definition Classes
    Variate
  4. val dt: Double
  5. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  6. def gen: Double

    Compute inter-arrival times of the NHPP.

    Compute inter-arrival times of the NHPP. 'tlast' is a global variable.

    Definition Classes
    NHPoissonProcessVariate
  7. 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 paramater.

    z

    the limit paramater

    Definition Classes
    NHPoissonProcessVariate
  8. def genTime: Double

    Compute arrival times of the NHPP.

  9. 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
  10. 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
  11. val lambda: VectorD
  12. val mean: Double

    Precompute the mean for the particular distribution.

    Precompute the mean for the particular distribution.

    Definition Classes
    TimeVariateVariate
  13. def meanF(tt: Double): Double

    Compute the mean as a function of time.

    Compute the mean as a function of time.

    tt

    the time point for computing the mean

    Definition Classes
    NHPoissonProcessTimeVariate
  14. def pf(k: Int, a: Double, b: Double): Double

    Compute the probability P[ (N(b) - N(a)) = k ].

    Compute the probability P[ (N(b) - N(a)) = k ].

    k

    the number of arrivals in interval [a,b]

    a

    the left end of the interval

    b

    the right end of the interval

    Definition Classes
    NHPoissonProcessTimeVariate
  15. def pf(k: Int, tt: Double): Double

    Compute the probability of k arrivals occurring in the time interval '[0, tt]'.

    Compute the probability of k arrivals occurring in the time interval '[0, tt]'.

    k

    the number of arrivals in the time interval

    tt

    the upper bound time value

    Definition Classes
    NHPoissonProcessTimeVariate
  16. def pf(k: Int): Double

    Compute the probability of k arrivals occurring in the time interval [0, t] where t is the current time.

    Compute the probability of k arrivals occurring in the time interval [0, t] where t is the current time.

    k

    the number of arrivals in the time interval

    Definition Classes
    NHPoissonProcessTimeVariate
  17. def pf(z: Double): Double

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

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

    z

    the mass point whose probability is sought

    Definition Classes
    TimeVariateVariate
  18. 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
  19. def productElementNames: Iterator[String]
    Definition Classes
    Product
  20. def reset(): Unit

    Reset the NHPP by resetting 'e' to zero.

    Reset the NHPP by resetting 'e' to zero.

    Definition Classes
    NHPoissonProcessTimeVariate
  21. 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
  22. 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
  23. val stream: Int