Sentence examples for binomial distribution in the from inspiring English sources

BinoGMF uses a binomial distribution in the generation of a GM from a normal distribution.

It is well known that a binomial distribution in the large number limit becomes a Gaussian.

For mutations that are predicted to inactivate a splice site, we assume a binomial distribution in the number of expected reads, based on the wild type allele.

We used negative binomial distribution in the model instead of Poisson distribution because the variance exceeds the mean and we observed overdispersion for the number of MRSA cases.

This distribution has the expected average CpG methylation fraction a i /(a i  +  b i ), with variance decreasing as a i  +  b i increases, approaching a binomial distribution in the limit of large a i  +  b i.

To assess the significance of the validation, we used the binomial distribution in which the expected pattern can either be confirmed or not between a pair of IPF and control cells (using data from 4E F).

Such a process would be modeled by a binomial distribution, in which the second observation lies between the values of the first and third observation only one-third of the time.

We constructed a 95% prediction interval by bootstrapping the data for lung cancer among women in Denmark, assuming that each cell followed a binomial distribution in which the incidence rate and the number of person-years at risk were used as probability of success and number of trials, respectively.

We asked how likely these counts were under a binomial distribution in which the probability of success is one minus the conversion rate, and we corrected this probability value for multiple testing (Benjamini and Hochberg 1995).

Analyses assumed a binomial distribution in which the 2 possible outcomes were to be either a case-patient or a control.

Recall that the binomial distribution (in equation 1) is the following: Prob (X = x | p ) = (n x )   p x (1 − p ) n − x Let the conjugate prior π(p| α, β) be a beta distribution (i.e., if p in equation 1 follows the beta distribution) (17) Beta (p | α, β ) = Γ p α − 1 (1 − p ) β − 1 where Γ is the gamma function.