Sentence examples for binomial distribution model from inspiring English sources

The P-value was calculated based on a negative binomial distribution model, and adjusted by Benjamini-Hochberg mothed.

About: Hotspot is a program for identifying regions of local enrichment of short-read sequence tags mapped to the genome using a binomial distribution model.

We propose a new collection frequency weighting scheme derived from the negative binomial distribution model of term occurrences.

Finally, to limit the effect of tumor heterogeneity and sampling heterogeneity, variants underpowered in matching FF or FFPE samples were filtered from the data set using a binomial distribution model (Supplementary Figure S19): the depth set was >70 × at the position of the variant for both FF and FFPE samples and the allelic fractions >0.067 in one of the two samples.

According to standard marketing theory, it's a complex problem, so you need a complex solution — for instance, the Pareto negative binomial distribution model, which has four parameters you estimate and gives you the probability for each customer that he or she will make future purchases.

This method was based on a negative binomial distribution model; the read count of gene i in the sample of j was designated Kij.

Binomial distribution models the probability of exactly k success in n trials, yielding the probability mass function parametrised by ρ as f k;rho)= C_{k}^{n}rho^{k}left 1-rho right)^{n}rho^{k}left 1-rho{n}= fright!}{k!(n-k)!}.

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.

DESeq implements a model based on negative binomial distribution; this model was developed with special attention to coping with biological variance and was run under R release 2.15.2.

For the contrast of the gene counts between inoculated and mock-inoculated samples we applied a statistical test implemented in the glm edgeR software [ 20, 71], which uses a negative binomial distribution to model the digital gene expression across conditions based on generalized linear models (glms) suitable for multifactor experiments of any complexity.

It uses a negative binomial distribution to model total variation.