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As for the mESC dataset, we found that the censored PCA approach yielded better posterior mean values for censored data points than the standard approach using substitution (Fig. 6A C).
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No studies have showed which approach yields better results, and all three have their critics and defenders.
It is concluded that the use of the element foundation stiffness approach yields better convergence at lower computation costs.
This new approach yields better agreement with the 3D simulation results than with axisymmetric and Cartesian 2D simulations.
The simulation results show that the proposed committee-based multi-agent approach yields better performance than the previous methods.
The equation was then applied to five stations and it was seen that the perturbation approach yields better statistical values compared to results determined using classical statistical approach.
Simulation results indicate that the novel approach yields better tracking performance and confirm the greater robustness to model and parametric uncertainties compared with a traditional nonlinear dynamic invention approach.
Results from the experiments suggest that the proposed approach yields better prediction performance than other state-of-the-art rating predictors which are based on the conventional Artificial Neural Network, Fuzzy C-Means, and Support Vector Machine approaches.
While the latter approach yields better performance it is also more complex.
We showed that the more complex approach yields better results with simulated data.
It is shown that the Stackelberg approach yields better payoffs for operators compared to the classical water-filling approach.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com