Poisson Process
A Poisson process is a stochastic process that models the occurrence of random events over time or space, characterized by independent increments and a constant average rate. It is widely used in probability theory, statistics, and operations research to analyze phenomena like customer arrivals, radioactive decay, or network traffic. The process assumes events happen independently, with the number of events in disjoint intervals following a Poisson distribution.
Developers should learn about Poisson processes when working on systems involving queuing theory, reliability engineering, or simulation modeling, such as in telecommunications, finance, or software performance testing. It is essential for predicting event frequencies, optimizing resource allocation, and designing scalable systems that handle random loads, like web servers or call centers.