Sentence examples for sum of means from inspiring English sources

ii) The slope of the log-log relationships between summed variances (or summed co-variances) and the squared sum of means approached one, indicating that a vast majority of correlations were linear.

For this calculation: (1) normalized recruitment as the total recruited nucleotides (kb) per kb of viral genome per Gb of virome (KPKG) was estimated for each virus genome, (2) mean normalized recruitment was calculated for each virus genomic data set (see also Supplementary Fig. 11) and (3) mean was normalized by the sum of means from all virus genomic data set expressed as relative recruitment.

That is, since an outgoing message of a given node has the mean equal to the sum of means of incoming messages to that node, given a reasonably high node degree, the outgoing message is approximately Gaussian.

It is important to note that cVC itself is not a mean or a sum, but a fine scale sum of means.

When more than one variable is used to characterize an ecological function, both the summed variance and co-variance components of the CV2 measure are expected to scale linearly with the squared sum of means.

More specifically, if the variables are either positively or negatively correlated with one another, the summed co-variances (expressed in absolute terms) reflect the summed variances and both components scale with the squared sum of means.

The inverse uncertainty propagation is given by the equally weighted sum of mean and covariance matrix objective functions.

The sum of mean square responses to stationary random excitations is minimized subject to the constraints on total stiffness capacity and total damper capacity.

The measurement score for evaluating these decision variables is the total cost: that is, the sum of mean cost, tolerance cost, quality loss, inspection cost, and failure cost.

In this case, Yin et al. [142] proposed a fitness function that is a weighted sum of mean tardiness and mean deviations of completion times.

Since the mean vectors and covariance matrices of the distribution are equal to the sum of mean vectors and covariance matrices of additive components, the model would be able to exploit contextual factors more efficiently.