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Computation of test sample size based on binomial formulation has been criticized as inadequate for assessment of the confusion among a large number (more than a dozen) of classes and for a large number of image pixels (e.g., [75]).
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A rationale for the appropriate choice of p, based on Binomial Probability is also presented.
An optional analysis method allowing for a better estimate of confidence bounds, based on binomial probability is presented.
The latter can be estimated by several approaches based on binomial and Poisson regression with or without constraints.
The logit models are based on binomial distributions (z-scores, generalized linear mixed model, GLMM).
In [15], the authors present a fault-tolerant broadcast algorithm for hypercubes based on binomial trees.
In the remaining part of the paper, we call our model the binomial model, since it is mainly based on binomial distribution.
Confidence intervals were calculated based on binomial and multinomial tests of accuracy.
The distribution of the mutations was based on binomial distribution.
Confidence intervals for proportions of detected samples were set at 95% and based on binomial distributions.
The exact CIs were calculated based on binomial distributions.
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