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,….

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.)

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.

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 ?
That is, the mean of the Poisson distribution would be [lambda] = 3.
Now, the total variation distance between the number of fixed points in a random permutation of size n, and the Poisson distribution with parameter 1, is less than the total variation distance between the numbers of fixed points in random permutations of sizes n and n + 1; this can be seen easily by noticing that [d.
Thus, for all cases, arrival data was successfully modelled as the Poisson distribution and the hypothesis was ascertained by evaluating chi-square tests.
3 indicates that despite of lack of any simple stochastic ordering, when a zero-truncated Poisson distribution has the same mean as that of its mixture distribution, sampling from the zero-truncated Poisson distribution seems to be more favorable than its mixture.
A standard approach is based on a mix between a Poisson distribution that models the number of accidents and a Bernoulli variable that models the probability p that the accidents will be filed.
The mean of a Poisson distribution is the only parameter necessary to completely describe the Poisson distribution.
To simulate an inhomogeneous L we may vary the parameter of the Poisson distribution in cubes.
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where c is the mean of the Poisson distribution and it can be either specified or replaced by an estimate c computed using an in-control baseline data.
New statistical capabilities, such as chi-square goodness-of-fit tests for single categorical variable and Poisson distribution -- Enhanced quality tools, including the addition of a one-sided tolerance option to crossed and nested Gage R&R commands -- Expanded graph editing capabilities
Underpinning the Poisson Distribution is the Poisson Process, a chance-generating mechanism that has an extremely simple axiomatic structure: