Sentence examples for multivariate normal variates from inspiring English sources

By the distribution theory for multivariate normal variates, the conditional distribution of (sqrt {nh^{d}}[hat {nu }_{j}(beta _{1},t -nu _{j}(beta _{1},t -nugiven (sqrt {nh^{d}}[hat {psi }_{j}(alpha,t)-psi _{j}(alpha,t)]) is asymptotically normal with mean rho_{alpha}^(Z_{j},t)frac{sigma_{1}(Z_{j},t)}{sigma_{2}(Z_{j},t)} sqrt{nh^{d}}[hat{psi}_{j}(alpha,t)-psi_{j}(alpha,t)].

Similar to BLIMP (Wen and Stephens, 2010), DIST uses the conditional mean formula for multivariate normal variates.

Σ is subsequently used to impute two-tailed Z-scores of unmeasured SNPs using the conditional expectation formula for multivariate normal variates (Lee et al., 2013).

DIST avoids the imputation of subject-level genotypes by directly applying, to unmeasured SNP statistics, the classical conditional expectation formula for multivariate normal variates.

This is achieved by (i) using the conditional expectation formula for multivariate normal variates and (ii) using the correlation structure from a relevant reference population.

Recently, researchers proposed a new MM-based imputation method, called BLIMP, which imputes genotypes/allele frequencies for unmeasured SNPs by using the conditional expectation formula for multivariate normal variates (Wen and Stephens, 2010).

Two multivariate normal random variates were generated, one to be the residual error of the latent intercept and one to be the residual error of the latent slope.

We may compute the correlation between Y 1,Y 2 for Z 1,Z 2 being independent standard normal variates.

The multivariate p.d.f of load cycles is modelled with a mixture of multivariate normal functions.

Two sets of n standard Normal variates are randomly generated (Nd 0,1), Nh 0,1)) and column bound to form matrix [N].

The random vectors are selected from a multivariate normal distribution.