scalation.random

PoissonProcess

Related Doc: package random

case class PoissonProcess(lambda: Double, stream: Int = 0) extends TimeVariate with Product with Serializable

This class generates arrival times according to a PoissonProcess. Given the current arrival time 't', generate the next arrival time.

lambda

the arrival rate (arrivals per unit time)

stream

the random number stream

See also

http://en.wikipedia.org/wiki/Poisson_process

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Serializable, Serializable, Product, Equals, TimeVariate, Variate, Error, AnyRef, Any
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  1. PoissonProcess
  2. Serializable
  3. Serializable
  4. Product
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  6. TimeVariate
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Instance Constructors

  1. new PoissonProcess(lambda: Double, stream: Int = 0)

    lambda

    the arrival rate (arrivals per unit time)

    stream

    the random number stream

Value Members

  1. final def !=(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  4. val MAXFAC: Int

    Attributes
    protected
    Definition Classes
    TimeVariate
  5. var _discrete: Boolean

    Indicates whether the distribution is discrete or continuous (default)

    Indicates whether the distribution is discrete or continuous (default)

    Attributes
    protected
    Definition Classes
    Variate
  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. def count(a: Double, b: Double): Int

    Definition Classes
    TimeVariate
  9. def count(tt: Double): Int

    Comute the mean as a function of time.

    Comute the mean as a function of time.

    tt

    the time point for computing the mean

    Definition Classes
    TimeVariate
  10. def discrete: Boolean

    Determine whether the distribution is discrete or continuous.

    Determine whether the distribution is discrete or continuous.

    Definition Classes
    Variate
  11. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  12. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  13. def flaw(method: String, message: String): Unit

    Show the flaw by printing the error message.

    Show the flaw by printing the error message.

    method

    the method where the error occurred

    message

    the error message

    Definition Classes
    Error
  14. def gen: Double

    Generate Poisson arrival times using and exponential random variable.

    Generate Poisson arrival times using and exponential random variable.

    Definition Classes
    PoissonProcessVariate
  15. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  16. 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
  17. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  18. val lambda: Double

    the arrival rate (arrivals per unit time)

  19. val mean: Double

    Pre-compute the mean for the particular distribution.

    Pre-compute the mean for the particular distribution.

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

    Compute the mean number of arrivals for amount of time tt.

    Compute the mean number of arrivals for amount of time tt.

    tt

    a number of intervals

    Definition Classes
    PoissonProcessTimeVariate
  21. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  22. final def notify(): Unit

    Definition Classes
    AnyRef
  23. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  24. 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 the interval

    a

    the left end of the interval

    b

    the right end of the interval

    Definition Classes
    PoissonProcessTimeVariate
  25. def pf(k: Int, tau: Double): Double

    Compute the probability P[ (N(t + tau) - N(t)) = k] using a general factorial function implemented with the Gamma function and Ramanujan's Approximation.

    Compute the probability P[ (N(t + tau) - N(t)) = k] using a general factorial function implemented with the Gamma function and Ramanujan's Approximation. Switches to pf_ln for k >= 170 to handle large k-values.

    k

    the number of arrivals in the interval

    tau

    the length of the interval

    Definition Classes
    PoissonProcessTimeVariate
  26. def pf(k: Int): Double

    Compute the probability P[ N(t) = k ] using a general factorial function implemented with the Gamma function and Ramanujan's Approximation.

    Compute the probability P[ N(t) = k ] using a general factorial function implemented with the Gamma function and Ramanujan's Approximation.

    k

    the number of arrivals in the interval

    Definition Classes
    PoissonProcessTimeVariate
    See also

    http://en.wikipedia.org/wiki/Poisson_process

  27. 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
  28. def pf_ln(k: Int, tau: Double): Double

    Compute the probability P[ (N(t + tau) - N(t)) = k] using the log of Ramanujan's Approximation formula.

    Compute the probability P[ (N(t + tau) - N(t)) = k] using the log of Ramanujan's Approximation formula.

    k

    the number of arrivals in the interval

    tau

    the length of the interval

  29. 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
  30. val r: Random

    Random number stream selected by the stream number

    Random number stream selected by the stream number

    Attributes
    protected
    Definition Classes
    Variate
  31. def reset(): Unit

    Reset the global time value to zero.

    Reset the global time value to zero.

    Definition Classes
    PoissonProcessTimeVariate
  32. val stream: Int

    the random number stream

  33. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  34. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  35. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  36. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from TimeVariate

Inherited from Variate

Inherited from Error

Inherited from AnyRef

Inherited from Any

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