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A Bayesian metamodel was developed, and model parameters were calibrated by maximizing a Gaussian likelihood function.
We construct constrained approximate optimal designs by maximizing a criterion subject to constraints.
The unknown parameters of FDMM are computed by maximizing a posteriori (MAP) estimation.
This new approach identifies the optimal design of an integrated biorefinery by maximizing a robustness index against uncertainties.
Multi-objective optimization of the tubes is performed by maximizing a composite objective including CFE and SEA.
We also tested a different method for the hyper-parameter estimation by maximizing a profile marginal likelihood and examined three methods dealing with the initialization.
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In the second step, the mole fractions of the molecules are adjusted by maximizing an information entropy criterion.
The estimation of these molar fractions is carried out by maximizing an information entropy criterion under linear constraints.
This novel sampling approach sequentially selects new points by maximizing an expected prediction error criterion that considers both the bias and variance information.
Although the asymptotic design is very simple and comes by maximizing an upper bound of the objective function, simulation results show that the asymptotic design performs well as the numerically optimal design.
Next, with the partial CSIT, the optimal structure of the source and relay precoders is also derived by maximizing an upper bound of the average MI between the source and the destination.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com