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Given a pool with s of n reads with the alternate allele, we used the binomial distribution to compute the probability of observing s or fewer successes in n trials with success probability.
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Frequency distribution was compared to binomial distribution to evidence the phase separation and atom clustering.
We have devoted two lines in Table 9 to the binomial distribution to illustrate its inaccuracy.
It uses a negative binomial distribution to model total variation.
Thus, we developed a model that uses a negative-binomial distribution to approximate an overdispersed Poisson distribution.
The binomial distribution was used to compute exact confidence intervals for proportions.
The binomial distribution was used to compute the significance level of the McNemar test.
We first present in details how the delta-method can be used to get a Gaussian approximation for the distribution of S N (using a binomial approximation to compute the pattern statistics).
We use a one-sided binomial test to compute a P -value for this deviation.
Significance of over-representation is estimated using binomial distribution by computing a nominal p-value.
Localized enrichments of sequence tags are identified based on a binomial distribution model computed against a local background model surrounding each tag.
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