Sentence examples for capture the variance of from inspiring English sources

The LEV-LBM is informed with local effective viscosities at each node to capture the variance of the mean free path of gas molecules in a bounded system.

PCA was executed on the correlation matrix of the water dataset, with the intention of identifying a reduced set of factors that could capture the variance of a dataset.

In our framework (Figure 7), the data is first transformed, using principal component analysis (PCA), from 3D coordinate space to another 3D eigen space, with the dimensions sorted in decreasing importance levels in capturing the variance of the atoms' movements.

Using latent composites of these outcome variables therefore appears to be a reasonable and practical solution to capturing the variance of a given construct comprehensively (hereafter, we use the term outcome-based composite to refer to variables representing the shared variance of construct-relevant outcomes).

Our measure of neuronal variability was derived from rsMEG variability, which we captured by the variance of the time series in standard frequency bands, analogously to the BOLD measure of RSFA.

The general hidden variable appears to capture changes in the variance of different groups of species biomass.

A representative of these methods is the eigenvoice method[1], where the low-dimensional manifold is a linear subspace and a set of linear bases (called eigenvoices), which capture most of the variance of the SD model parameters, can be obtained by principal component analysis.

PCA is used to reduce the number of correlated variables into a smaller number of artificial variables that capture most of the variance of the original variables while being uncorrelated with each other (Hatcher 1994).

Principal-component analysis (PCA) reduces the number of correlated variables into a smaller number of artificial variables (factors) that capture most of the variance of the original variables while being uncorrelated with each other (Hatcher 1994).

The Bayesian information criterion (BIC) [ 23], which trades off variance explained versus model complexity (number of principal components retained), can be used to determine objectively the number of these dimensions that capture most of the variance of the set of shapes.

In PCA, the eigenvector with largest eigenvalue captures most of the variance of dataset.