Sentence examples for distribution of the coefficient from inspiring English sources

Considering that the Meridian distribution has the characteristic of high energy concentration, we propose to adopt the Meridian prior as the prior distribution of the coefficient vector.

The spatial as well as the temporal distribution of the coefficient was observed to be dominated by coherent structures and showed a strong dependency on M and θ.

Compared with MOEA/D, distribution of the coefficient vectors is more uniform over the design space, and the population size neither increases nonlinearly with the number of objectives nor considers a formulaic setting.

In this paper, considering that the Meridian distribution has the characteristic of high energy concentration, we proposed to adopt the Meridian prior as the prior distribution of the coefficient vector when a problem of sparse representation is to be solved.

Figure 7 summarizes the distribution of the coefficient of variation of the five unit test case metrics studied according to the three variables (internal software class attributes) used in the univariate clustering.

Likewise, the posterior distribution of the coefficient of variation estimated here strongly renders a global molecular clock assumption untenable, and the calculated covariance among inferred branch rates suggest there is little evidence for an ancestor-descendant autocorrelation of rates in the phylogeny used in this study.

After divisive normalization transform, we find that the marginal distribution of the coefficients is approximately Gaussian distributed.

The Laplacian distribution model can be used to match the distribution of the coefficients.

This joint modeling decomposes the overall distribution of the coefficients into the superposition of individual cluster distributions.

Figure 12 reveals a close match between the empirical and Weibull-based PDFs, supporting the assumption of the Weibull distribution of the coefficients.

Figure 2 presents an example of a real trajectory matrix (left), the representations coefficients L (center), and the singular value distribution of the coefficients (right).