Poisson distribution


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Related to Poisson distribution: binomial distribution, Poisson process

Pois·son dis·tri·bu·tion

(pwah-son[h]'),
1. a discontinuous distribution important in statistical work and defined by the equation p (x) = e μx/ x!, where e is the base of natural logarithms, x is the sequence of integers, μ is the mean, and x! represents the factorial of x.
2. a distribution function used to describe the occurrence of rare events, or the sampling distribution of isolated counts in a continuum of time or space.
Farlex Partner Medical Dictionary © Farlex 2012

Poisson distribution

A sampling distribution based on the number of occurrences, r, of an event during a period of time, which depends on only one parameter, the mean number of occurrences in periods of the same length.
Segen's Medical Dictionary. © 2012 Farlex, Inc. All rights reserved.

Poisson distribution

Statistics The distribution that arises when parasites are distributed randomly among hosts. See Distribution.
McGraw-Hill Concise Dictionary of Modern Medicine. © 2002 by The McGraw-Hill Companies, Inc.

Poisson distribution

(statistics) the frequency of sample classes containing a particular number of events (0,1,2,3 … n), where the average frequency of the event is small in relation to the total number of times that the event could occur. Thus, if a pool contained 100 small fish then each time a net is dipped into the pool up to 100 fish could be caught and returned to the pool. In reality, however, only none, one or two fish are likely to be caught each time. The Poisson distribution predicts the probability of catching 0,1,2,3 … 100 fish each time, producing a FREQUENCY DISTRIBUTION graph that is skewed heavily towards the low number of events.
Collins Dictionary of Biology, 3rd ed. © W. G. Hale, V. A. Saunders, J. P. Margham 2005

Poisson,

Siméon Denis, French mathematician, 1781-1840.
Poisson distribution - a discontinuous distribution important in statistical work.
Poisson ratio
Poisson-Pearson formula - to determines the statistical error in calculating the endemic index of malaria.
Medical Eponyms © Farlex 2012
References in periodicals archive ?
We note that, x is a variable that is subject to the neutrosophic Poisson distribution, the distribution parameter is
[P.sub.oisson] denotes the probability of k nodes, [beta] denotes the parameter of Poisson distribution. N(t) denotes the number of nodes at time t and it utilized to count the number of nodes.
From (17), {[x.sub.n]ln([[lambda].sub.b] + [[lambda].sup.i.sub.s][h.sub.i]([[theta].sub.n])) - [[lambda].sup.i.sub.s][h.sub.i]([[theta].sub.n])[DELTA]t} obeys the Poisson distribution. When N is large enough, [J.sub.i](X) obeys the Gaussian distribution with the PDF:
Bivariate Zero-Inflated Poisson Distribution. Here, we use a bivariate zero-inflated Poisson (BZIP) model, which is a mixture of bivariate Poisson and a point mass at (0, 0).
The optimal parameters of the proposed plan can be determined plan for specified requirements under the conditions of gamma prior and Poisson distribution. The rest of the paper is set as: a brief introduction about the RGS plan under gamma-Poisson distribution is given in Section 2.
Dispersion index values close to 1 and u values between [+ or -] 1.96 indicate conformity with the Poisson distribution. Values of u higher than 1.96 indicate an overdispersion of data, whereas u values lower than -1.96 indicate an underdispersion (1,11).
In tau-leap method the number of times a reaction fires is a random variable from a Poisson distribution with parameter [a.sub.r] ([bar.x]) [tau].
Also utilized in this study were various statistical methods, including least-square linear regression analysis, Fisher's exact test for 2x2 contingency tables, Spearman's rho, Kendall's tau, the Bernoulli process, and the Poisson distribution.
We assumed that [deaths.sub.i] followed a Poisson distribution with mean given by the product of the death rate [r.sub.i] and the population size [D.sub.i], which we estimated to be five times the population size in 2001.
This renders the Poisson distribution inappropriate for modeling count data in such instances.