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Therefore, the following CHIAMO parameter set, which minimized the discrepancy in genotype calls between the two platforms in an unbiased fashion, was used to call the genotypes: -gz -max1 -max2 -nmax 200 -n 0 -b 0 (which is the same as used in a previous study [2] except for the omission of allele frequency priors).
It must be considered that both trials were rigorous clinical trials and greatly minimized the discrepancy between prescribed and delivered doses.
Parameter settings that minimized the discrepancy between the historic reported case counts and those output by the model, were determined by a sequence of 50 optimizations using the Powell global optimization algorithm available in Vensim DSS for Windows (version 5.5c).
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Constitutive parameters have been identified by minimizing the discrepancy between model and experimental data.
The imitation process is modeled as a hierarchical optimization system, which minimizes the discrepancy between two multi-dimensional datasets.
Based on this result, an optimal feedback quantizer which minimizes the discrepancy is derived in a closed form.
The methodology of calibration is to minimize the discrepancy between the experimental hysteresis loops and the corresponding numerical results using Particle Swarm Optimization (PSO) algorithm.
The subsystems minimize the discrepancy between their own local variables and the corresponding allocated values, and then return the optimization results to main optimization.
The dielectric profile of the system is then determined by minimizing the discrepancy between these two calculations simultaneously for multiple positions of the test charge.
For minimizing the discrepancy functional we use the adjoint problem method; however, we use it not only in iterative regularization, but in Tikhonov and parametric ones as well.
Pursing of simple and direct synthesis route is vital and desirable to minimize the discrepancy between electrodes, and is essential for implementing of reliable detection.
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