# Poisson distribution

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Related to Poisson distribution: binomial distribution, Poisson process

## Poisson distribution

[pwä′sōn‚dis·trə′byü·shən]
(statistics)
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,….

## 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.

### REFERENCES

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.)

## Poisson distribution

(mathematics)
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.

## 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.
References in periodicals archive ?
Since dataset S consists of observations from both the in-control and out-of-control processes, we need to remove the out-of-control subsamples before fitting a Poisson distribution to the data.
Next, we calculate the node distribution in each group using the Poisson distribution according to [12].
The marginal distributions of BZIP model are zeroinflated univariate Poisson distribution, whose probability mass function is
When p varies from lot-to-lot at random and is distributed as gamma distribution which is the natural conjugate prior for sampling from the Poisson distribution, the density function of prior distribution of p is given by
ji], the total number of deaths among commuters from j to i, was assumed to follow a Poisson distribution with mean [[mu].
Poisson distributions for number of tropical cyclones, number of hurricanes, number of major hurricanes, and number of U.
For example, in the case of the number of sexual partners in the past year, the non-risk group are individuals who never had extra sexual partners beyond their spouse or significant other; these non-risk individuals substantially inflate the number of zero results beyond what is under the Poisson distribution.
For the Poisson distribution the interest is in a random variable, X, that represents the number of occurrences of an event within a fixed time.
The linear correlation observed when the number of positive samples remains below the limit of 30% coincides with the Poisson distribution being nearly linear in this range.
The key implication of equation (1) is that individual and grouped data can both be analyzed with the Poisson distribution (32).
The width of the bar in Poisson distribution function curve (Fig.
The layer lines taken every 6000 successive measurements show a fine structure which builds up over time instead of cancelling out as in the case of a typical random or Poisson distribution.

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