Sentence examples for binomial distribution is given from inspiring English sources

and y i | x i ~ Poisson μ i with μ i ~ Gamma shape = ϕ, scale = 1 / ϕ = Gamma mean = 1, variance = 1 / ϕ and y i | x i ~ NegBin μ i, ϕ with E y i | x i = μ i and Var y i | x i = μ i + μ i 2 / ϕ ; y i = 0, 1, 2, …. the probability density function of the negative binomial distribution is given by: f y, θ, μ = Γ ϕ + y Γ ϕ y !

The skewness of a binomial distribution is given by the formula (1 - 2 q ) / m q p. When p > q, and thus 1 > 2 q, the skew is positive.

The mean μ and the variance σ2 of the Poisson-binomial distribution are given by equation (1) and (2), respectively.

The binomial distribution is now a conditional probability, given the value of r.

To estimate the number of replicate reactions required to achieve a particular number of successful amplifications (at a given probability), the binomial distribution is appropriate.

Nevertheless, for co-dominant markers such as SNPs and given the usual range of FIS values, the binomial distribution is fairly reasonable [12], [19].

Given PolyPhen's false positive rate on neutral substitutions being 0.08, the p-value for observing this data from a binomial distribution is as low as 1.30E-13.

A standardized binomial distribution is a binomial distribution scaled and translated so that it has zero mean and unit variance.

Binomial distribution is a fundamental statistical assumption about sampling process.

An extension of the binomial distribution is the beta-binomial distribution.

As already seen, the negative binomial distribution can be given a an accident proneness and a "spells" interpretation in the context of accident theory in terms of a gamma mixed Poisson distribution and a Poisson distribution generalized by a logarithmic distribution (Kemp [1967]).