Poisson distribution

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Related to Poisson distribution: binomial distribution, Poisson process

Pois·son distribution

(pwä-sôN′)
n. Statistics
A probability distribution which arises when counting the number of occurrences of a rare event in a long series of trials.

[After Siméon Denis Poisson (1781-1840), French mathematician.]

Poisson distribution

(ˈpwɑːsən)
n
(Statistics) statistics a distribution that represents the number of events occurring randomly in a fixed time at an average rate λ; symbol P0(λ). For large n and small p with np = λ it approximates to the binomial distribution Bi(n,p)
[C19: named after S. D. Poisson]

Pois•son′ distribu`tion

(pwɑˈsoʊn, -ˈsɔ̃)
n.
a probability distribution whose mean and variance are identical.
[1920–25; after S. Dutch. Poisson (1781–1840), French mathematician and physicist]
ThesaurusAntonymsRelated WordsSynonymsLegend:
 Noun 1 Poisson distribution - a theoretical distribution that is a good approximation to the binomial distribution when the probability is small and the number of trials is largedistribution, statistical distribution - (statistics) an arrangement of values of a variable showing their observed or theoretical frequency of occurrencestatistics - a branch of applied mathematics concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate population parameters
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The Poisson distribution was assigned based on a modified park test (Manning and Mullahy 2001), and we applied a generalized chi-square scale parameter to address overdispersed data in the Poisson distribution (McCullagh and Nelder 1989).
Without loss of generality, we model the data with a Poisson distribution and estimate the Poisson parameter [lambda] using the Maximum Likelihood Estimation (MLE) :
We evaluate the performance of the three-dimensional Markov chain based on the Poisson distribution for the node distribution and velocity.
Poisson distribution p([lambda]), with parameter [lambda] > 0, with pdf
They assume that the number of hospitalizations follows a Poisson distribution, with a lognormal prior for the mean number of hospitalizations.
However, the long held paradigm in the communication and performance communities that voice traffic and, data follow Poisson distribution is inaccurate and inefficient.
They provide estimates not only for the probability that none of the events occur, but also for the difference between the generating function of N and that of the corresponding Poisson distribution over the interval [0, 1] (see  for more details).
n] is a random variable which obeys the Poisson distribution.
Using the MGF of the Poisson distribution in (18), the MGF of I is finally given by
Poisson regression model belongs to the family of generalized linear models in which the response variable is a count one and has followed Poisson distribution.

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