Sentence examples for variance of counts from inspiring English sources

From the overall results, it was observed that the Poisson distribution performs better, where the difference between mean and variance of counts of threshold exceedances is small otherwise the NB distribution is found to be efficient when used in combination with generalized logistic distribution in the PDS model, and this is more prominent for μ < 1.4.

The variance of counts was computed as in [ 58].

While the Poisson regression (which uses a Poisson distribution) is commonly used for analyses of count data, it does not handle over-dispersed data sets well due to the assumption that the variance of counts equals the mean.

Extra-dispersion (overdispersion or underdispersion) is a common phenomenon in practice when the variance of count data differs from that of a Poisson model.

We therefore considered other convenient but less restrictive models where Variance of count in week t = dispersion × average count in week t (1) Models of this form are called quasi-Poisson.

DESeq was used to normalise read counts for differences in sequencing depth between samples, to robustly estimate the variance of read counts between samples, and to variance-stabilise and log-transform the read count data [ 54].

In order to reduce the variance of tag counts and the computational complexity of training and decoding, we discretize the original tag counts into 20 bins according to the genome-wide distribution.

This can potentially induce a lower-than-expected X-statistic for high counts and a correspondingly inflated p-value; however, the application of Coates' correction algorithm increases the variance of the counts beyond what would be expected for Poissonian data, so that this form of bias does not arise.

Notably, the joint Gaussianity of spike counts in bins of size T where T is much larger than the intrinsic time constant τ s ( T ≫ τ s ) also implies that models of multi-neuronal dynamics only need to consider the mean and variance of spike counts because all higher cumulants are zero.

The model was able to capture the relationship between the mean number of CELs and the variance of these counts.

The variance of word counts is very close to the mean, which is expected from the Poisson distribution.