Poisson process Created 2026-09-24 Updated 2026-10-14
A Homogeneous Poisson process models arrivals at a constant rate . Its counting process counts arrivals in , with . Its paths are right-continuous nondecreasing functions, take nonnegative integer values, and have unit jumps almost surely. The defining Poisson distribution of each increment isIt has stationary increments: the probability distribution above depends on the duration , not the starting time . It also has independent increments: counts in any finite collection of disjoint time intervals are independent random variables. Together these conditions specify the joint count laws, not merely the Poisson distribution at a single time.[ref]
Poisson Processes (Chapter 2). pp. 78-79: counting-process definition and its equivalence to exponential interarrival times
The rate has units of arrivals per unit time; is dimensionless. The Poisson distribution givesFor a toy rate per minute and a duration of minutes, the expected value and variance of the count are both . The probability of no arrival is , and the probability of exactly three is . In two consecutive one-minute intervals, the probability of exactly two arrivals in the first and exactly one in the second is , using independent increments. These are exact model calculations with rounded decimals, not measured frequencies.
Synthetic arrival times and their right-continuous counting path, compared with the exact mean at two arrivals per minute
. Details. The diagram's nine hand-chosen arrivals illustrate a path; the line is an expected value, not a prescribed trajectory. Constant rate does not imply evenly spaced arrivals. Poisson interarrival times connect the count description to waiting, while Poisson thinning explains how independently routing arrivals produces separate streams. This makes the Poisson process useful as a simple arrival model for a queue, provided constant rate and independent increments are plausible. For a deterministic changing rate, use an Inhomogeneous Poisson process; bunching or history-dependent arrivals need additional assumptions. Conditional on , the arrival times are the order statistics of independent uniform distributions on , as developed in Poisson process conditional arrival times.
