Sentence examples for denote the variance from inspiring English sources

Let Var(D) denote the variance of D. We have text{Var}(D =text{Var}(D_{1}).

We denote the variance of A i ( d ) and A i ( c ) by σ a, i ( d ) and σ a, i ( c ), respectively.

where T is the period of time between measurements, q1 and q2 denote the variance of the acceleration noise and the noise in target return intensity, respectively.

where g x and g w denote the variance of the prediction error for speech and noise, respectively; A x = ∑ k = 0 p a x k e − jωk ; and A w = ∑ k = 0 q a w k e − jωk.

In the following, we denote the variance of the channel estimation error of the initialization stage by σ Δ h 2 ( 0 ) and it must satisfy the following conditions: σ Δ h 2 ( 0 ) is located in the interval 0, σ Δ h max 2, namely σ Δ h 2 ( 0 ) < σ Δ h max 2. (26) This condition ensures a reasonably good initialization performance of the iterative estimator.

Let σe denote the variance in the environmental variable and σg denote the variance in the genetic variable.

For a subset of vertices (Asubset N), let (Sigma _{A}) denote the variance-covariance matrix of the variables in (X_{A}), and define with (S_{A}) the corresponding observed variance-covariance submatrix.

where β d and β r denote the variances of the direct path and the residual reverberation part, respectively.

The variables ({sigma _{s}^{2}}) and ({sigma _{n}^{2}}) denote the variances of primary user signal and noise, respectively.

where p denotes a transformed probability map, m1 p and m2 p are the mean values of the nuclear and cytoplasmic pixels in the transformed map such as the pixel in the regions shown in Fig. 7 a, and s1 p and s2 p denote the variances of the nuclear and cytoplasmic pixels in the transformed map, respectively.

As in the introduction, we denote the variance-covariance matrix for the breeding values by G (5) G = var u [ a ] = var u [ W ˜ u ] = W ˜ var [ u ] W ˜ ′ = σ u 2 m ˜ − 1 W ˜ W ˜ ′ The subscript u on the variance operator indicates that it is with respect to the random genetic effects and not the genotypes the latter are simply given and not assumed to follow any distribution.