Sentence examples for in our approximation from inspiring English sources

In our approximation algorithm, users are allocated airtime according to the max-min fair allocation policy.

If the points in our approximation problem have not the same importance the neighborhoods E k can be adjusted to reflect it.

The synthetic accessibility score – SAscore in our approximation is calculated as a combination of two components: The fragmentScore, as already mentioned, was introduced to capture the "historical synthetic knowledge" by analyzing common structural features in a large number of already synthesized molecules.

The assumptions made in our approximation are similar to those used in TIGAR, but the empirical comparisons herein show that our proposed method performs better in terms of computation time and required memory, while also providing improved accuracy on real and simulated data.

In our approximations, (mathcal{K}_{k}) of (3.10) has fewer terms than is used in [3].

In summary, our approximation to A2 and the observed frequency – may be written in the form (14) A 2 = f A (n, p 0, μ m, μ p ) Δ, (15 Ω = ω - f Ω(n, p 0, μ m, μ p ) Δ, and substituting (14) into (7) gives an approximation for B (16) B 2 = (ω 2 + μ p 2 ) f A (n, p 0, μ m, μ p ) Δ, where f A and fΩ are positive functions defined in terms of the model parameters and Δ = τ - τ cr.

Similarly as in [4], our approximation will be applied by considering the split extension.

In contrast, our approximation was remarkably accurate for large effective population sizes irrespective of the strength of selection.

In total, our approximation (model A) yields an error of ±1 4 meV, which is smaller than the intrinsic errors arising from the choice of the DFT functionals.

Using the values of s ( V ( μ i, t ) ) to approximate the integral in Equation 11, we are in fact including the influence of all other neurons (an infinite number of them in the continuum limit), not just those that we have retained in our reduced approximation.

Roughly speaking, (1.17) tells us that the Kolmogorov distance in this version of the central limit theorem is attained at the mean of the respective distributions; whereas according to Theorem 1.1 and Corollary 1.2, the Kolmogorov distance in our Poisson approximation setting is attained at the mean the standard deviation of the corresponding distributions.