Poisson distribution

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Related to Poisson distribution: binomial distribution, Poisson process

Poisson distribution

A probability distribution whose mean and variance have a common value k, and whose frequency is ƒ(x) = k x e -k / x !, for x = 0,1,2,….
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
The following article is from The Great Soviet Encyclopedia (1979). It might be outdated or ideologically biased.

Poisson Distribution


one of the most important probability distributions of random variables that assume integral values. A random variable X that obeys a Poisson distribution takes on only nonnegative values; the probability that X = k is

where λ is a positive parameter. The distribution is named after S. D. Poisson; it first appeared in a work by him published in 1837. The mathematical expectation and variance of a random variable having a Poisson distribution with the parameter λ are equal to λ. If the independent random variables X1 and X2 have Poisson distributions with parameters λ1 and λ2, their sum X1 + X2 has a Poisson distribution with the parameter λ1 + λ2. Illustrations of Poisson distributions are given in Figure 1.

Figure 1

In probability-theoretic models, the Poisson distribution is used both as an approximating and an exact distribution. For example, if the events A1,…, An occur in n independent trials with the same low probability p, the probability that k of the n events simultaneously occur is approximated by the function pk(np); the mathematical significance of this assertion for large n and 1/p is stated by Poisson’s theorem. In particular, such a model is an excellent description of the process of radioactive decay and of numerous other physical phenomena.

The Poisson distribution is used as an exact distribution in the theory of stochastic processes. An example is the calculation of the load on communication lines. The number of calls made in nonoverlapping intervals of time are usually assumed to be independent random variables that obey a Poisson distribution with parameters whose values are proportional to the lengths of the corresponding intervals.

The arithmetic mean X̄ = (X1 + … + Xn)/n of n observed values of the random variables X1,…, Xn is used as an estimate of the unknown parameter λ, since this estimate is unbiased and has a minimum standard deviation.


Gnedenko, B. V. Kurs teorii veroiatnostei, 5th ed. Moscow-Leningrad, 1969.
Feller, W. Vvedenie v teoriiu veroiatnostei i ee prilozheniia, 2nd ed., vol. 1. Moscow, 1967. (Translated from English.)
The Great Soviet Encyclopedia, 3rd Edition (1970-1979). © 2010 The Gale Group, Inc. All rights reserved.

Poisson distribution

A probability distribution used to describe the occurrence of unlikely events in a large number of independent trials.

Poisson distributions are often used in building simulated user loads.

This article is provided by FOLDOC - Free Online Dictionary of Computing (foldoc.org)

Poisson distribution

A statistical method developed by the 18th century French mathematician S. D. Poisson, which is used for predicting the probable distribution of a series of events. For example, when the average transaction volume in a communications system can be estimated, Poisson distribution is used to determine the probable minimum and maximum number of transactions that can occur within a given time period.
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References in periodicals archive ?
If an event is repeated in time according to the neutrosophic Poisson distribution:
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With respect to the Poisson distribution fit, chi-square results greater than those of the negative binomial fit were obtained (Table 2), indicating the best fit of the negative binomial, confirmed by the aggregation indices.
As [x.sub.n] obeys the Poisson distribution with the arriving rate [[lambda].sub.n]([theta])[DELTA]t, the PDF of [x.sub.n] is shown in (4).
Bivariate Poisson Distribution. The discrete random variables of [Y.sub.1] and [Y.sub.2] are bivariate Poisson distribution and are shown as ([Y.sub.1], [Y.sub.2]) ~ BP([[lambda].sub.1], [[lambda].sub.2], [[lambda].sub.3]) if their joint probability mass is given by
Actually, nuclear disintegrations obey Poisson statistics, but if the number of observed events is large, the Poisson distribution is sufficiently approximated by a normal distribution.
In the Bayesian theory, it is found that the gamma distribution is a natural conjugate prior for the sampling from a Poisson distribution. When the sample items are drawn randomly from a process, the number of defects (or nonconformities) in the sample is distributed according to Poisson law, and the gamma distribution is the conjugate prior to the average number of non-conformities per item.
The latter is usually reported as the 95% confidence interval (CI) and is associated with two components: the distribution of unstable aberrations in the irradiated sample (corresponding to the Poisson distribution or the overdispersion) and uncertainties associated with the calibration curve (6).