Sentence examples for distribution of variance from inspiring English sources

We note that all of these uncertainties can be visualized in the source-type diagram as scatter plots (e.g., Aso and Ide 2014), density contours, or the distribution of variance reductions (e.g., Nayak and Dreger 2015).

After simulation on two different sets of whitened natural scenes (trees/plants with edges and short line segments and landscapes with less fine structure), it was found that the distribution of variance for trees/plants was similar for all three models.

The distribution of the source mechanism indicates that the normal-fault are probable mechanisms to explain the observations, although we cannot constrain the strike direction, which is presented as nearly invariant for the strike angle in Additional file 1: Figure S3, which shows the comparison of the distribution of variance reductions as a function of the fault parameters.

The log-transformation was used to normalize the skewed distribution of variance.

The DFA analysis indexes the relative distribution of variance within the data across a range of different time-scales.

The mean number of photons recorded from single spots was set to about 250 with a normal distribution of variance of 50 photons.

However, the distribution of variances has a broad tail and many sites showed extremely variable methylation patterns.

We subsequently compared the variance of guild proportions in the original data matrix to the distribution of variances from the null matrices.

Variances of expression profiles of genes in the diagonal of C are normalized so that their values reflect their quantile in the whole distribution of variances [9] (i.e. these values range from 0 to 1).

One advantage of this framework is that it allows us to incorporate a distribution for the phylogenetic tree (in terms of a distribution of variance-covariance matrices Σ) and then to integrate over this distribution to take into account all the possible trees: (4) f (θ, y ) = p ∫ L (y | θ, Σ ) p dΣ where Σ stands for the variance-covariance matrix (as in Eqn. 1) associated with each tree.

The distribution of variances of those concentration estimates among the 6 sample preparations are shown in Figure 8.