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Sensitivity of the new filter to the size of parameter uncertainty, in terms of the variance of parameter perturbations, is also considered.
Recently, the ensemble modeling (EM) has been considered to cope with large-scale kinetic modeling by successively reducing the size of parameter space based on the available experimental data such as fluxes and/or intracellular metabolite concentrations together with thermodynamic constraints for the direction of the net fluxes [98].
The process is controlled by the size of parameter Lambda.
This restriction is necessary to keep the size of parameter tables for the P values calculations manageable.
Quantitative sex differences, are tested by estimating the size of parameter estimates for the genders ('the common effects sex limitation model').
Because we need to specify a ω ij interval for all possible n2 regulators of a GRN, large ω ij intervals have a considerable impact on the computational complexity (size of parameter solution space) of the model inference algorithm.
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Multiple subsequent runs of each dataset were then performed and effective sample size of parameters (ESS) and trend plots examined to assure proper mixing and convergence.
At least five independent runs of each data set were performed and effective sample size of parameters (ESS) and trend plots examined to assure proper mixing and convergence.
In addition, word recall, verbal fluency, and numeracy were measured on different scales and this makes direct comparisons between the effect sizes of parameter estimates for the association between oral health markers and various cognitive outcome measures difficult.
The parameterized textures are compressed as a multidimensional Laplacian pyramid on fixed size blocks of parameter space.
Convergence was assessed by comparison of the two runs, while the adequacy of mixing was investigated by checking whether the effective sample sizes of parameters exceeded 200.
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