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To estimate the model parameters, we use SASeis2006 (Ogata 2006), which estimates the five ETAS parameters (μ, K, c, α, p) by minimizing the value of the Akaike Information Criterion (Akaike 1974) with a fast likelihood algorithm (Ogata et al. 1993).
Parameters w,b,k1,kd were computed iteratively with a simulated annealing scheme [37] by minimizing the value of the objective function G (Eq. 2) and using gene expression profiles of the cyclins measured by Pramila et al.Simulated annealing allowed the discovery of a global minimum in a parameter space given by the parameters of Eq. 1 and the objective function G (Eq. 2).
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The best fits listed in Table 1 were determined by minimizing the values of Rfactor and reduced χ for fits with reasonable values of σ.
The robustness of the objective function is achieved by minimizing the worst value of unsatisfactory degree functions of the uncertainty parameters while the feasibility robustness is ensured by a sub-optimization conducting the worst-case analysis.
In this paper, we propose a fuzzy minimax clustering model by minimizing the maximum value of the set of weighted cluster variations in such a way that they satisfy a prior distribution.
The model and discretization are applied to both the deterministic and the stochastic continuous restricted location problem, where the latter is converted into a deterministic equivalent problem by minimizing the expected value of the objective function weighted by the probabilities of scenarios.
The ground state impurity energy is evaluated by minimizing the expectation value of the Hamiltonian in Equation 2 with respect to λ.
The MMSE equalizer also takes into account the noise statistics and performs an overall optimization by minimizing the expected value of the squared error, E{||x−W MMSE-TD y||2}.
The dielectric functions of SRO were extracted by minimizing the RMSE value to fit the ellipsometric data of the SRO buffer layer to a three-medium optical model consisting of a semi-infinite STO substrate/SRO film/air ambient structure.
Au et al. [20] proposed a robust design method based on the convex model and achieved the robustness of the objective function by minimizing the worst value of unsatisfactory degree functions of the uncertain parameters and ensured the feasibility robustness by a sub-optimization conducting the worst-case analysis.
The optimization process started by minimizing the χ2 value across all parameter combinations at a low resolution, to avoid falling in a valley of low χ2 values that did not contain the lowest value.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com