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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Instance Constructors
- new PoissonProcess(lambda: Double, stream: Int = 0)
- lambda
the arrival rate (arrivals per unit time)
- stream
the random number stream
Value Members
- final def !=(arg0: Any): Boolean
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- final def ##: Int
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- final def ==(arg0: Any): Boolean
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- val MAXFAC: Int
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- protected
- Definition Classes
- TimeVariate
- var _discrete: Boolean
Indicates whether the distribution is discrete or continuous (default)
Indicates whether the distribution is discrete or continuous (default)
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- protected
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- Variate
- final def asInstanceOf[T0]: T0
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- def clone(): AnyRef
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- protected[lang]
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
- def count(a: Double, b: Double): Int
- Definition Classes
- TimeVariate
- 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
- def discrete: Boolean
Determine whether the distribution is discrete or continuous.
Determine whether the distribution is discrete or continuous.
- Definition Classes
- Variate
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- final def flaw(method: String, message: String): Unit
- Definition Classes
- Error
- def gen: Double
Generate Poisson arrival times using and exponential random variable.
Generate Poisson arrival times using and exponential random variable.
- Definition Classes
- PoissonProcess → Variate
- 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
- PoissonProcess → Variate
- final def getClass(): Class[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
- Annotations
- @native() @HotSpotIntrinsicCandidate()
- 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
- 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
- final def isInstanceOf[T0]: Boolean
- Definition Classes
- Any
- val lambda: Double
- val mean: Double
Precompute the mean for the particular distribution.
Precompute the mean for the particular distribution.
- Definition Classes
- TimeVariate → Variate
- 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
- PoissonProcess → TimeVariate
- final def ne(arg0: AnyRef): Boolean
- Definition Classes
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- final def notify(): Unit
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- @native() @HotSpotIntrinsicCandidate()
- final def notifyAll(): Unit
- Definition Classes
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- @native() @HotSpotIntrinsicCandidate()
- 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
- PoissonProcess → TimeVariate
- 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
- PoissonProcess → TimeVariate
- 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
- PoissonProcess → TimeVariate
- See also
http://en.wikipedia.org/wiki/Poisson_process
- 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
- TimeVariate → Variate
- 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
- 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
- def productElementNames: Iterator[String]
- Definition Classes
- Product
- val r: Random
Random number stream selected by the stream number
Random number stream selected by the stream number
- Attributes
- protected
- Definition Classes
- Variate
- def reset(): Unit
Reset the global time value to zero.
Reset the global time value to zero.
- Definition Classes
- PoissonProcess → TimeVariate
- 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
- 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
- val stream: Int
- final def synchronized[T0](arg0: => T0): T0
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- final def wait(arg0: Long, arg1: Int): Unit
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- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
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- final def wait(): Unit
- Definition Classes
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- def finalize(): Unit
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- Deprecated