PoissonProcess
Related Doc:
package random
Indicates whether the distribution is discrete or continuous (default)
Indicates whether the distribution is discrete or continuous (default)
Comute the mean as a function of time.
Comute the mean as a function of time.
the time point for computing the mean
Determine whether the distribution is discrete or continuous.
Determine whether the distribution is discrete or continuous.
Show the flaw by printing the error message.
Show the flaw by printing the error message.
the method where the error occurred
the error message
Generate Poisson arrival times using and exponential random variable.
Generate Poisson arrival times using and exponential random variable.
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.
the arrival rate (arrivals per unit time)
Pre-compute the mean for the particular distribution.
Pre-compute the mean for the particular distribution.
Compute the mean number of arrivals for amount of time tt.
Compute the mean number of arrivals for amount of time tt.
a number of intervals
Compute the probability P [ (N(b) - N(a)) = k ].
Compute the probability P [ (N(b) - N(a)) = k ].
the number of arrivals in the interval
the left end of the interval
the right end of the interval
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.
the number of arrivals in the interval
the length of the interval
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.
the number of arrivals in the interval
http://en.wikipedia.org/wiki/Poisson_process
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.
the mass point whose probability is sought
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.
the number of arrivals in the interval
the length of the interval
Return the entire probability mass function (pmf) for finite discrete RV's.
Return the entire probability mass function (pmf) for finite discrete RV's.
number of objects of the first type
Random number stream selected by the stream number
Random number stream selected by the stream number
Reset the global time value to zero.
Reset the global time value to zero.
the random number stream
This class generates arrival times according to a
PoissonProcess. Given the current arrival time 't', generate the next arrival time.the arrival rate (arrivals per unit time)
the random number stream
http://en.wikipedia.org/wiki/Poisson_process