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.

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.

Poisson distribution

Statistics The distribution that arises when parasites are distributed randomly among hosts. See Distribution.

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.

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.

Poisson distribution

a statistical distribution which often describes the sampling frequency of individual, isolated counts in time and space.
References in periodicals archive ?
In adaptive modeling, we use the Metropolis-Hastings (MH) algorithm in MCMC simulation to continuously update the estimate of the Poisson distribution parameter [lambda].
We use the same assumptions in [12] for our model analysis to estimate the average node using the Poisson distribution.
The moments of bivariate zero-inflated Poisson distribution are given as [23]
p](0|[mu]) under the Poisson distribution by p to account for structural zeros.
The Poisson distribution is discrete and is used to calculate probabilities related to the number of occurrences of an event within a fixed unit of time.
1 and 2 correspond to regions of space passing the Earth that have preferred/discrete velocities, and not random ones, as randomly distributed velocities would result in a Poisson distribution, i.
It is only slightly less well known that the number of fixed points in a random permutation of size n follows a distribution that, as n goes to infinity, converges to the Poisson distribution with parameter 1.
His work, most familiar in the context of the Poisson Distribution, addresses precisely these issues.
The Poisson distribution is generally defined on the integer range 0, 1, .
The Poisson distribution can be applied to systems with a large number of possible outcomes, each of which is rare.
In these analyses game scores were modelled assuming a distribution appropriate for count data, the Poisson or over-dispersed Poisson distribution.