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The Poisson distribution was used instead of the binomial distribution to derive cumulative probabilities, because the total number of patients in the SHI population was unknown.
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We use binomial distribution [21] to derive the expected number of packets received by node v j from node v i.
The dNdS ratio was determined for each codon and where dN was greater or less than dS, a p value was derived from a two-tailed binomial distribution to assess the significance.
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.
To quantify these results, we compared the frequency count at each voxel (out of a maximum frequency of 10) against the binomial distribution and derived a p value.
The baySeq assumes that subsets of the libraries share the parameters of Negative Binomial distribution and derives an empirical prior distribution for the corresponding parameter sets.
We used multiple binomial logistic regression to derive our index.
The negative binomial distribution can be derived from a two-stage model for the distribution of a discrete variable Y (Venables and Ripley [2002]).
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