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A visual depiction of a Poisson point process starting. In probability theory, statistics and related fields, a Poisson point process (also known as: Poisson random measure, Poisson random point field and Poisson point field) is a type of mathematical object that consists of points randomly located on a mathematical space with the essential feature that the points occur independently of one ...
Particle decay is a Poisson process, and hence the probability that a particle survives for time t before decaying (the survival function) is given by an exponential distribution whose time constant depends on the particle's velocity:
Shot noise or Poisson noise is a type of noise which can be modeled by a Poisson process. In electronics shot noise originates from the discrete nature of electric charge . Shot noise also occurs in photon counting in optical devices, where shot noise is associated with the particle nature of light.
Pages in category "Poisson point processes" The following 17 pages are in this category, out of 17 total. This list may not reflect recent changes. ...
One point process that gives particularly convenient results under random point process operations is the Poisson point process, [2] The Poisson point process often exhibits a type of mathematical closure such that when a point process operation is applied to some Poisson point process, then provided some conditions on the point process ...
It describes how a Poisson point process is altered under measurable transformations. This allows construction of more complex Poisson point processes out of homogeneous Poisson point processes and can, for example, be used to simulate these more complex Poisson point processes in a similar manner to inverse transform sampling.
This page was last edited on 16 May 2016, at 17:44 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply ...
The poisson clumping heuristic (PCH), published by David Aldous in 1989, [7] is a model for finding first-order approximations over different areas in a large class of stationary probability models. The probability models have a specific monotonicity property with large exclusions .