Packages

c

scalation.random

PoissonProcess

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

Linear Supertypes
Serializable, Serializable, Product, Equals, TimeVariate, Variate, Error, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. PoissonProcess
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. TimeVariate
  7. Variate
  8. Error
  9. AnyRef
  10. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

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
    @native() @throws( ... )
  8. def count(a: Double, b: Double): Int
    Definition Classes
    TimeVariate
  9. 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
  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. final 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. 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
    PoissonProcessVariate
  16. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  17. 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
  18. 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
  19. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  20. val lambda: Double
  21. val mean: Double
    Definition Classes
    TimeVariateVariate
  22. 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
  23. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  24. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  25. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  26. 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
  27. 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
  28. 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

  29. 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
  30. 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

  31. 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
  32. val r: Random

    Random number stream selected by the stream number

    Random number stream selected by the stream number

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

    Reset the global time value to zero.

    Reset the global time value to zero.

    Definition Classes
    PoissonProcessTimeVariate
  34. 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
  35. 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
  36. val stream: Int
  37. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  38. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  39. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  40. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @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

Ungrouped