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We modeled the abundance distribution of k-mers as a mixture of over-dispersed Poisson (negative binomial) distributions in which the mixture coefficients represent amounts of sequence at each copy number and the overdispersion parameter accommodates uneven read-depth across the target genome.
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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).
Analyses assumed a binomial distribution in which the 2 possible outcomes were to be either a case-patient or a control.
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 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).
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.
This gives rise to the negative binomial (NB) distribution in which the variance increases as a quadratic function of the mean, i.e., Var y) = μ + φμ[ 7].
Binomial distribution, in statistics, a common distribution function for discrete processes in which a fixed probability prevails for each independently generated value.
BinoGMF uses a binomial distribution in the generation of a GM from a normal distribution.
The binomial distribution in 2013 is then replaced by a beta-binomial.
We focus on the Negative Binomial distribution in what follows.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com