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
- Alphabetic
- By Inheritance
- PoissonProcess
- Serializable
- Serializable
- Product
- Equals
- TimeVariate
- Variate
- Error
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Instance Constructors
-
new
PoissonProcess(lambda: Double, stream: Int = 0)
- lambda
the arrival rate (arrivals per unit time)
- stream
the random number stream
Value Members
-
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
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
-
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
-
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
- val lambda: Double
-
val
mean: Double
- 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
-
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
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