What is a Poisson process?

  subject area
  in short
For a Poisson process, events occur randomly and are independent of each other.

For probabilistic modeling of engineering systems, the Poisson process is a very important stochastic process if not the most important one. It can be used to model the occurrence of independent random events along a continuous axis. For example, the Poisson process can be used to model occurrences of earthquakes or accidents in time, as well as the occurrence of defects along a pipeline or breakdowns per driven distance.

In order to model occurrences with a Poisson process, the underlying process must have the following properties:

  1. Independent occurrences: The number of occurrences in two non-overlapping intervals must be statistically independent.
  2. Probability of occurrences proportional to duration: In an interval $[t,t+\Delta t]$, the probability of exactly one occurrence is asymptotically proportional to the interval length $\Delta t$, as $\Delta t \to 0$.
  3. Occurrences do not coincide: The probability of two or more occurrences within a sufficiently small interval must be orders of magnitude lower than the probability of one occurrence.

A Poisson process can be quantified through its rate of occurrence $\lambda$. If the rate is constant, the process is called a homogeneous Poisson process. If the rate varies with time or space, the process is referred to as a non-homogeneous Poisson process. If not explicitly mentioned otherwise, we restrict the discussion to the homogeneous case.

The following random variables are often used in combination with an underlying Poisson process:

  • The number of occurrences in the interval $[0,t]$ follows a Poisson distribution with mean $\nu=\lambda t$.
  • The time/distance to the first event and
  • the time/distance between successive events follow an exponential distribution with rate $\lambda$.
  • The time/distance to the $k$th occurrence follows a Gamma distribution with shape parameter $k$ and rate parameter $\lambda$, such that the mean is $k/\lambda$.
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