Sentence examples for models count data from inspiring English sources

The DESeq package models count data using a negative binomial distribution [ 51].

The DESeq models count data using a negative binomial distribution, with variance and mean linked by local regression [ 61].

It models count data as negative binomial (NB) distributed (Equation 5), and employs an empirical Bayes procedure to moderate the degree of over-dispersion across the exons.

EdgeR models count data using on overdispersed Poisson model, and use an empirical Bayes procedure to moderate the degree of over-dispersion across genes.

Recently, a new differential expression calling algorithm, edgeR [ 27], has been proposed, which models count data using a negative binomial model that can be regarded as an over-dispersed Poisson model.

Differentially-expressed genes were called at a false discovery rate (FDR) of 1% using the DESeq package in R [ 14], which models count data by a negative binomial distribution.

The Poisson distribution is often used for modeling count data (McCullagh and Nelder [1989]): 194 ff.

The CMP distribution has quickly grown in popularity because of its ability to model count data in a flexible manner.

The Poisson distribution is one of the most popular discrete distributions, serving as a natural, classical distribution to model count data.

The sum-of-Conway-Maxwell-Poissons (sCMP) class of distributions is a flexible construct for modeling count data that captures several well-known distributions as special cases: the Poisson, negative binomial, binomial, geometric, Bernoulli, and Conway-Maxwell-Poisson (CMP).

Poisson processes are commonly used to model count data (data in which the observations can take only the non-negative integer values) [11, 54] in many domains, such as modeling rare incidents in psychiatric hospitals [45], and traffic analysis [52, 55].