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Variance of a plus b is variance of a plus variance of b plus 2 times covariance a b.
where is energy per bit and is variance of noise in each dimension.
where σ si 2 is variance of SI noise, e.g., of dark current noise and k is a proportionality factor for SD component [9, 15].
This is variance of
Dimensionality reduction using principal component analysis (PCA) is based on a very important trait that is variance of the data.
BayxSha QTL variance components: Vg/Vp = 0.016 Ve/Vp = 0.1675 Vge/Vp = 0.014 Vr/Vp = 0.864* LerxCol QTL variance components: Vg/Vp = 0.05 Ve/Vp = 0.266 Vge/Vp = 0.0744 Vr/Vp = 0.6094* *Vg is variance of genetic main effects, Vp is phenotypic variance, Ve is environmental (UV-B) effects, Vge is variance of genotype-by-environment interaction effects, Vr is residual variance.
Similar(51)
σ n 2 and I are variance of noise and identity matrix, respectively.
∑ is (k−1) dimensional diagonal matrix with (i,i) element being variance of i-th element (i=1,2,...,k−1) of proportion vector P1−P2.
where and are variances of and.
Although there are variances of centroid positions among similar silhouettes, from observations, we can say that centroids are still reliable.
and (sigma ^{2}_{x}), (sigma ^{2}_{hat {x}}), and (sigma ^{2}_{w}) are variances of transmitted symbols, decoded symbols, and noise, respectively.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com