Sentence examples for binomial distribution to model from inspiring English sources

It uses a negative binomial distribution to model total variation.

They employ a negative binomial distribution to model both biological and technical noise in the total counts of expressed genes.

DESeq uses a negative binomial distribution to model genic read counts following normalisation based on size factors and variance.

Because it was not, we used a Poisson, rather than a negative binomial, distribution to model the count outcome variables.

First, we analyzed the count data with the Bioconductor package edgeR[ 20], which uses the negative binomial distribution to model the count data.

Taking into account the over-dispersed distribution of RD values (Anders and Huber, 2010), we use a negative binomial distribution to model RD signals.

Using beta binomial distributions to model predictions and errors works well, however, and the ratio of such distributions can be used to estimate the likelihood that a given prediction will be incorrect.

In principle, it is preferable to model the counts as a mixture of negative binomial distributions to model over dispersion of the count data.

Two other approaches: Maq (Li,H. et al., 2008) and SOAPSNP (Li,R. et al., 2008) have proposed using Binomial distributions to model genotypes; however, these were developed in the context of sequencing normal genomes, not cancer genomes.

We use a beta-binomial distribution to model the probability of the evidence, given a genotype that matches the reference.

Previous studies have used the beta-binomial distribution to model the count data underlying ASE measurements (e.g. Skelly et al. 2011).