Sentence examples for mean vector zero from inspiring English sources

As pointed out in Eq. (1) above the error term of the two activities will have a bivariate normal distribution with mean vector zero and covariance matrix: left{ {begin{array}{*{20}c} { in_{a} } { in_{f} } end{array} } right./X_{a},X_{f} /} sim Nleft( {left( {begin{array}{*{20}c} 0 0 end{array} } right).

Error terms have a standard normally distribution with mean vector zero and a covariance matrix with diagonal elements equal to 1.

where y ∈ R n is the vector of n observations, X ∈ R n × p is the known design matrix, β ∈ R P is the unknown vector of regression coefficients and ε ∈ R n is the error vector with mean vector zero and the covariance matrix Σ.

The phenotypic values for TPCs are simulated by summing the QTL genotypic curves and multivariate-normally distributed residual errors with mean vector zero and covariance matrix Σ structured by the AR(1) model.

Log measurements for genes in a given gene set in condition 1 (condition 2) are simulated as multivariate normal with mean vector zero and covariance matrix Σ1 (Σ1).

Given a feature vector x, the histogram flatness score is defined as ((mathbf {m}_{nontext}-mathbf {m}_{text})^{T} Lambda _{F}^{-1}mathbf {x}), where (hat {mathbf {m}}_{nontext}) and (hat {mathbf {m}}_{text}) are the estimated mean vector for the two classes; and (hat {Lambda }_{F}) is the estimated common covariance matrix.

This results in a zero mean vector of observations [6] and successively a zero mean matrix.

Gaussian distribution with zero mean vector and covariance matrix β -1 I.

Specifically, ψdB=10 log10ψ and ψ dB ∼ N 0, σ dB 2. The other three random variables ρ n s, 0 2, ρ ̇ n s, 0 2, and ρ ̈ n s, 0 2, which, in turn, are related to the noise process n(t) have a joint Gaussian distribution with zero mean vector and covariance matrix Σ ρ, i.e., ρ n s, 0 ρ ̇ n s, 0 ρ ̈ n s, 0 ∼ N 0, Σ ρ, (56).

By further defining the block source signal s f ( i ) = [ s ( iM ) T s ( iM + 1 ) T … s ( iM + M − 1 ) T ] T ∈ ℂ KM, we have u f ( i ) = ( F ∗ ⊗ I K ) s f ( i ) [9], which is a zero mean vector with E [ u f ( i ) u f ( i ) ∗ ] = ( F ∗ ⊗ I K ) ( F ∗ ⊗ I K ) ∗ = ( F ∗ F ) ⊗ ( I K I K ) = I KM according to assumption (A).

For the last 50 observations, i.e. the controls, the mean vector was set to zero,.