Sentence examples for a vector of observed from inspiring English sources

U i is a vector of latent variables and Z i is a vector of observed, fixed covariates.

Assume that the count a and b are generated according to Poisson distributions with mean π at a and π bt b, respectively (2) Let x = (a,b,t a, t b) be a vector of observed data.

For the ith individual, Y i is a vector of observed outcomes, X i is a vector observed exposure indicators.

First, these were adjusted for fixed effects using a linear model with fixed effects only; y =  Xb +  e, with y being a vector of observed survival times, X being an incidence matrix linking survival time observations to fixed interaction effects of laying house-row-level, b being a vector of the fixed effects, and e is the residual term.

Assume input and outputs for j = 1,…,n DMUs (X j,Y j ) where X j  = (x 1j,…,x ij,…,x mj ) is a vector of observed inputs and Y j  = (y 1j,…,y rj,…,y sj ) is a vector of observed outputs for DMU j.

Representative utility is usually specified to be linear in parameters ( V_{ijm} = beta^{prime} x_{ijm} ), where ( x_{ijm} ) is a vector of observed variables relating to alternative m.

However, what grid graphs embrace is how many "steps" separate a vector of genotypes observed in individual i from an observed vector of genotypes in individual j.

Let be a vector of fully observed variables that predict whether.

We also compared the goodness-of-fit of a mixed model using realized or expected relationships: (1) y = µ 1 n + Za + ϵ where y is the vector of observed phenotypic values of n seedlings; μ is an intercept, 1 n is a vector of 1s; Z is the known design matrices relating to a, the unknown vector of random additive genetic effects with a ~ N 0, A σ a 2 ) or a ~ N 0, G σ a 2 ).

We fitted animal models with random effects and fixed effects: y = Xb+Zaa+Zcc+Zmm+Znn+e, where y is the vector of observed phenotypic values of the individuals and vectors b = fixed effects, a = additive effects, c = permanent environment effects, m = maternal effects, n = common-nest effects and e = residual effects.

The general linear model in matrix form can be written as (1) Y = X β + ε, where Y is the vector of observed pixel values, β is the vector of parameters, and ε is the vector of error terms.