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
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new
PoissonProcess(lambda: Double, stream: Int = 0)
- lambda
the arrival rate (arrivals per unit time)
- stream
the random number stream
Value Members
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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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- TimeVariate
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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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- Variate
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final
def
asInstanceOf[T0]: T0
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def
clone(): AnyRef
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def
count(a: Double, b: Double): Int
- Definition Classes
- TimeVariate
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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
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def
discrete: Boolean
Determine whether the distribution is discrete or continuous.
Determine whether the distribution is discrete or continuous.
- Definition Classes
- Variate
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final
def
eq(arg0: AnyRef): Boolean
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def
finalize(): Unit
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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
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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
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final
def
getClass(): Class[_]
- Definition Classes
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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
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final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
- val lambda: Double
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val
mean: Double
- Definition Classes
- TimeVariate → Variate
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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
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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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
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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
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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
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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
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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
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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
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val
r: Random
Random number stream selected by the stream number
Random number stream selected by the stream number
- Attributes
- protected
- Definition Classes
- Variate
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def
reset(): Unit
Reset the global time value to zero.
Reset the global time value to zero.
- Definition Classes
- PoissonProcess → TimeVariate
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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
- val stream: Int
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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