Examples of 'poisson distribution' in a sentence
Meaning of "poisson distribution"
The Poisson distribution is a probability distribution that expresses the likelihood of a certain number of events occurring within a fixed interval of time or space, when the events are rare and randomly distributed. It is often used in various fields, such as statistics, mathematics, and physics, to model and analyze events that happen randomly over time or in space
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- Any of a class of discrete probability distributions that express the probability of a given number of events occurring in a fixed time interval, where the events occur independently and at a constant average rate; describable as a limit case of either binomial or negative binomial distributions.
How to use "poisson distribution" in a sentence
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poisson distribution
We can contrast this with a poisson distribution.
Poisson distribution is a good approximation to the binomial when n is large.
This follows a statistical poisson distribution.
The poisson distribution may be regarded as a special case of the binomial distribution.
In this dissertation we present a review of the generalized poisson distribution.
See also poisson distribution.
It is also a special case of a compound Poisson distribution.
Hence a Poisson distribution is not an appropriate model.
A tη i which represents the Poisson distribution law.
The Poisson distribution occurs as a limit of binomial distributions.
It is a particular case of the compound Poisson distribution.
Application of the Poisson distribution to reliability.
The confidence intervals are based on Poisson distribution.
Such as a Poisson distribution or a normal distribution.
Generate random numbers from a Poisson distribution.
See also
Is also a Poisson distribution with parameters.
Confidence intervals were based on the Poisson distribution.
The Poisson distribution in probability theory is named after him.
I assumed that goals had a Poisson distribution.
A Poisson distribution with parameter λ is approximately normal for large λ.
Probability calculations are based on the Poisson distribution.
A Poisson distribution with paraméter λ is approximately normal for large λ.
Since then we recover the Poisson distribution.
We found that using a Poisson distribution for modeling conversions performs moderately.
Titer at endpoint was calculated using a Poisson distribution.
The Poisson distribution can be derived as a limiting case of the binomial distribution.
The mean and variance of a Poisson distribution is λ.
The Poisson distribution can be used to approximate probabilities for a binomial distribution.
This distribution would be governed by the Poisson distribution.
This is a special case where the Poisson distribution is almost a Gaussian distribution.
The data are found to be reasonably represented by a Poisson distribution.
Fitting a truncated Poisson distribution to a truncated count distribution gives an estimated Poisson parameter.
This is easily answered looking at the Poisson distribution.
Poisson Distribution is a simple predictive model that does not allow for numerous factors.
Confidence intervals were calculated according to the Poisson distribution.
A Poisson distribution is a necessary and sufficient condition that all detections are statistically independent.
Relative populations are expected to follow the Poisson distribution.
A property of the Poisson distribution is that the mean and the variance are equal.
We also tested each part of the Poisson distribution.
Intuitively understand why the Poisson distribution is the limiting case of the binomial distribution.
Bortkiewicz showed that those numbers follow a Poisson distribution.
Alternatively other distributions such as Poisson distribution can be used to describe the probability distribution.
And this is important to our derivation of the Poisson distribution.
Compounding a Poisson distribution with rate parameter distributed according to a gamma distribution yields a negative binomial distribution.
The expected distribution can be found using Poisson distribution.
The Poisson distribution is characterized by a Poisson parameter denoted by λ.
Bortkiewicz showed that those numbers followed a Poisson distribution.
The Poisson distribution arises in connection with Poisson processes.
Confidence intervals for counts were based on the Poisson distribution.
Where Sn has Poisson distribution with mean nμ.
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I would actually like some soupe de poisson
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