Sentence examples for binomial distributions to model from inspiring English sources

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.

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.

DEseq uses a generalisation of the Poisson model, the negative binomial distribution, to model biological and technical variance and test for differential expression between two experimental conditions.

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

Finally, our use of the binomial distribution to model read counts may underestimate the variance; e.g. due to factors such as PCR artifacts.

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