Sentence examples for model in equations from inspiring English sources

To improve estimation time and accuracy, the maximum simulated likelihood (MSL) is employed using Geweke-Hajivassiliou-Keane (GHK) simulator in estimating the model in equations (1) and (3) (Roodman [2011]; Hajivassiliou et al. [1996]).

As shown e.g., in [16], the measurement model in Equations 1 and 2 can be recast as a sparse linear problem by defining an overcomplete dictionary of steering vectors evaluated over a set of possible spatial frequencies Ω = {ν1, …, ν G }.

According to the Callaway model in Equations 3 and 4, the first term c 1 d 1 + 1 d 2 represents the boundary scattering; the second term A ω4 represents the scattering by point impurities or isotopes, and the third term B ω 2 T e - θ D 3 T represents the Umklapp process.

The probability p L i which is obtained in the E-step is utilized to combined with the contour prior p s i of the object and the prior probability P N I of the Potts model to obtain the MRF model in Equations (7) and (8).

We presented a general model in equations 1 and 2 that is essentially hierarchical, and even made some explicit prior assumptions about the variant effects distribution (e.g., a point mass with no variability).

The kinetic parameters in the steady state model in equations (5) and (6) are replaced by the parameter perturbations shown in (7) for robust design of the genetic circuit.

The measurement model in Equation 1 and the random signal model are adopted.

The results obtained by estimating the model in equation 5 are reported in Table 8.

Therefore, for the remainder of this study we used the gravity model in Equation (2).

Some consideration on the linear model in Equation 6 should now be done.

The results of estimating the difference in-differences modifference in-differenceseported in Table 615.