Sentence examples for parameters we defined from inspiring English sources

Prior to the optimization of atomic parameters, we defined a total of 52 and 36 atom types to represent a variety of chemical circumstances in FSD and SAMPL4 molecules, respectively.

For defining model evaluation parameters, we defined the following terms according to Keramati et al. (2014): True negative (TN) refers to the number of negative tuples that were labeled correctly by the classifier.

Based on these available parameters, we defined three criteria.

As biomechanical parameters, we defined the maximum load (breaking load: F max) applied at fracture of the grafted cartilage.

With the optimized parameters, we defined the connectivity at site j0 as a ratio of the marginal log likelihoods: where Θ′ correspond to a recombinant of Θ at site j0.

As in previous studies assessing time-varying metabolic parameters, we defined discrete time intervals in which we assumed the metabolic model parameters to be approximately constant: dinner interval (6 p.m .11 p.m)., early nighttime interval with a transition time (TN) (11 p.m.– TN), late night/early morning (TN 8 a.m ., and breakfast (8 a.m.–12 p.m).

Since the design criterion depends on the unknown parameters, we define relative efficiency of a design and consider minimax and Bayesian criteria to find designs that are robust for a range of parameter values.

To estimate all these parameters, we define a penalty function of residuals as chi^{2}(mathbf{g};mathbf{k};alpha,beta,gamma) = sum_{i=1}^{N_{text{vec}}} boldsymbol{epsilon}_{i} cdot boldsymbol{epsilon}_{i} + sum_{i=1}^{N_{text{scal}}} {f_{i}^{2}} (4).

For D feature parameters, we define the random vector X=(X 1,X 2,…,X D ) as the feature vector, and x(k) as an observation vector in the k th video segment.

If θ= θ1,…,θ n ) is a vector of n parameters, we define e θ = ∑ i = 1 e θ i, so that e θ ×ξ gives the proportional change of π C ∗ that would be obtained if all parameters θ1,…,θ n were simultaneously changed by the same proportion ξ.

Given a model that involves d parameters, we define a parameter space as (1) Θ d = Θ 1 × Θ 2 × ⋅ ⋅ ⋅ × Θ d, where Θ i is the interval of the real numbers ℝ for which the parameter θ i is defined.