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Both the proposed re-parameterization methods achieve a good degree of output prediction at a greatly decreased computational cost.
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The proposed re-engineering is cost oriented.
It is shown that both the re-parameterization and the use of the Sobolev cost function gradient are efficient for solving such an ill-posed inverse problem.
To solve this issue, Lu and Ades proposed a re-parameterization of the heterogeneous variance model in which each between-trial variance parameter would be represented by the sum of variances of the two involved treatment arms minus the corresponding covariance [ 10].
We propose a re-parameterization of the traditional mean difference based effect measure by using the log ratio of means as an effect size measure for each gene in each study.
In this work, we propose a re-parameterization method to handle non-Gaussian parameters via Ensemble Kalman Filter framework.
Numerical examples are given to illustrate the proposed parameterization.
Subjective listening tests were conducted to verify the performance of the proposed parameterization methods.
Non-linear fitting is used to test the proposed parameterization performance before the actual optimization.
Although the original problem is convex, the re-parameterization is not, and local maxima of the latter problem are also characterized in this section.
A higher level switching structure between the various estimation schemes is used to supervise the re-parameterization of the adaptive controller in real time.
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