PoissonProcess
The PoissonProcess class generates arrival times according to a Poisson Process. Given the current arrival time 't', generate the next arrival time.
Value parameters
- lambda
-
the arrival rate (arrivals per unit time)
- stream
-
the random number stream
Attributes
- See also
- Graph
-
- Supertypes
-
trait Serializabletrait Producttrait Equalsclass TimeVariateclass Variateclass Objecttrait Matchableclass AnyShow all
Members list
Value members
Concrete methods
Generate Poisson arrival times using and exponential random variable.
Generate Poisson arrival times using and exponential random variable.
Attributes
Determine the next random number for the particular distribution. This version allows one paramater.
Determine the next random number for the particular distribution. This version allows one paramater.
Value parameters
- z
-
the limit paramater
Attributes
Compute the mean number of arrivals for amount of time 'tt'.
Compute the mean number of arrivals for amount of time 'tt'.
Value parameters
- tt
-
a number of intervals
Attributes
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.
Value parameters
- k
-
the number of arrivals in the interval
Attributes
- See also
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.
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.
Value parameters
- k
-
the number of arrivals in the interval
- tau
-
the length of the interval
Attributes
Compute the probability P [ (N(b) - N(a)) = k ].
Compute the probability P [ (N(b) - N(a)) = k ].
Value parameters
- a
-
the left end of the interval
- b
-
the right end of the interval
- k
-
the number of arrivals in the interval
Attributes
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.
Value parameters
- k
-
the number of arrivals in the interval
- tau
-
the length of the interval
Attributes
Reset the global time value to zero.
Reset the global time value to zero.
Attributes
Inherited methods
Attributes
- Inherited from:
- TimeVariate
Count then number of sample until the time exceeds tt.
Count then number of sample until the time exceeds tt.
Value parameters
- tt
-
the time point
Attributes
- Inherited from:
- TimeVariate
Determine whether the distribution is discrete or continuous.
Determine the next random integer for the particular distribution. It is only valid for discrete random variates.
Determine the next random integer for the particular distribution. It is only valid for discrete random variates.
Attributes
- Inherited from:
- Variate
Determine the next random integer for the particular distribution. It is only valid for discrete random variates. This version allows one parameter.
Determine the next random integer for the particular distribution. It is only valid for discrete random variates. This version allows one parameter.
Value parameters
- z
-
the limit parameter
Attributes
- Inherited from:
- Variate
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.
Value parameters
- z
-
the mass point whose probability is sought
Attributes
- Inherited from:
- TimeVariate
Return the entire probability mass function (pmf) for finite discrete RV's.
Return the entire probability mass function (pmf) for finite discrete RV's.
Value parameters
- k
-
number of objects of the first type
Attributes
- Inherited from:
- Variate
Attributes
- Inherited from:
- Product
Attributes
- Inherited from:
- Product
Determine the next random string for the particular distribution. For better random strings, overide this method.
Determine the next random string for the particular distribution. For better random strings, overide this method.
Attributes
- Inherited from:
- Variate
Determine the next random string for the particular distribution. For better random strings, overide this method. This version allows one parameter.
Determine the next random string for the particular distribution. For better random strings, overide this method. This version allows one parameter.
Value parameters
- z
-
the limit parameter
Attributes
- Inherited from:
- Variate
Inherited fields
Attributes
- Inherited from:
- TimeVariate
Precompute the mean for the particular distribution.