Sentence examples for matrix calculated as from inspiring English sources

where du,vindicates the probabilities in the confusion matrix, calculated as shown in Table 1.

A is the technology coefficient matrix, calculated as the product of the input output transaction matrix T and the inverse matrix (widehat{{mathbf{X}}}^{ - 1}) of diagonalised total industry output (i.e. ({mathbf{A}} = {mathbf{T}} cdot widehat{{mathbf{X}}}^{ - 1})).

The error-bars cited on the fit parameters were taken from the formal covariance matrix, calculated as the inverse of the Jacobian matrix of χ2 multiplied by its own complex conjugate.

G was a genomic relationship matrix, calculated as done in Yang et al.

Then breeding values for both phenotyped and non-phenotyped individuals can be predicted by solving the equations for model 1 above: where G is the realised relationship matrix calculated as above, and is a genetic variance.

The two models only differed in their distributional assumptions for the additive breeding values: PED : a ~ N 0, A σ a 2, IBD ‒ GS : a ~ N 0, G IBD σ a 2, where σ a 2 is the additive genetic variance, A is the pedigree-based numerator relationship matrix, and G IBD is the IBD-based genomic relationship matrix, calculated as described above.

Corresponding estimated covariance matrices were calculated, as well as lower and upper bounds for power.

Corresponding estimated covariance matrices were calculated, as well as lower and upper bounds for power using the methods presented in section 'Approximate UNIREP power confidence intervals UNIREP power confidence intervals'.

The proposed object valid points matrix V pro is calculated as, V_{text{pro}} = Bleft( {M,t_{1} } right) circ neg Bleft( {I_{text{diff}},t_{text{in}} } right) (20 where I diff is the intensity difference map calculated from Eq. (18) and t in is the intensity threshold.

Population structure was estimated from the three principal components of a principal component analysis (PCA) based on a correlation matrix calculated from genetic markers, as recommended by Bradbury et al. (2007).

We used this clustering index in each cell of the field of study–occupation matrix, calculated for native-born workers, as a benchmark reflecting the common matching quality in Canadian labor markets that internationally educated immigrant workers could achieve in the long run.