Sentence examples for binomial distribution is modelled from inspiring English sources

The binomial distribution is modelled by a logit link function, while the Poisson distribution is modelled by a log link function.

Unlike previous approaches using Poisson distributions, more flexible negative binomial distribution is adopted to model over-dispersed RD signals.

While this estimation procedure determines a geometric model to be the best model within the sCMP class, the negative binomial distribution is another viable model, as determined by Burnham and Anderson (2002); see Table 5.

Therefore, the binomial distribution is a conservative model for the sampling process.

Internally, a negative binomial distribution is adopted by DESeq to model the discrete read counts in each sample and account for the overdispersion of the coverage data.

If these distributions were returns on an investment or gamble, then by comparing their moments an investor could determine that the binomial distribution is a superior investment model.

For over-dispersed data examples, the negative binomial distribution is generally expected to be a good model to describe the distribution.

The negative binomial distribution is used commonly throughout biology as a model for overdispersed count data, with attention focused on the negative binomial dispersion parameter, k.

Costs were log-transformed due to the skewed distribution of medical expenditures, and the negative binomial distribution was used to model LOS to account for overdispersion.

A negative binomial distribution was adopted to model the data due to the presence of extra-Poisson variation in the case counts.

A binomial distribution was then used to model the false positive rate in SNP/SNV detection, given the average error frequency (p) and local sequencing depth (n).