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Our splitting method uses weights from the binomial distribution as component weights.
We determined 95% confidence intervals from the binomial distribution as described by Clopper and Pearson (19 ).
The p-value is calculated from binomial distribution, as is the case for single-end enrichment.
ψ Based on estimation from above studies using a binomial distribution as the confidence interval.
Single-proportion CIs were derived from the binomial distribution with continuity correction.
Confidence intervals for the test-set estimates were obtained from the binomial distribution, using the R function binom.test.
(iv) Simulation of methylated read counts: we use a binomial (or truncated negative binomial) distribution as the emission distribution of the non-homogenous discrete-state HMM.
The Beta distribution over μ j is conjugate to the Binomial distribution over θ ji, so we can write the posterior distribution as a Beta distribution.
The estimated frequencies determined by the binomial distribution, while better than those from the Poisson and negative binomial models, still deviate considerably in comparison to the sCMP class.
The approximation is based on the binomial distribution, derived as follows.
Diagnostic accuracy was estimated using sensitivity, specificity and predictive values with 95% CIs calculated from the binomial distribution and assuming laboratory results as the gold standard.
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