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A small number of training samples are created by random approach with FEA codes for building the Gaussian process regression (GPR) surrogate model, and the highly nonlinear and implicit performance function is approximated by GPR with an explicit formulation under a small sample condition.
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A statistical significance was achieved by generating each test set 30 times, including: (i) a pairwise generation with random values, (ii) a random generation with random values and (iii) both generations together (values are selected by the pairwise approach and by the random approach).
We account for these three issues by proposing a random effects approach with a bias correction for cases which are difficult to estimate due to low or high rates of non-participation or HIV prevalence.
Interestingly, the maximum entropy design performed comparably with the random approach in this dataset.
Missing data were treated with a missing at random approach.
A random approach: Don't ask, just turn up with a dish.
I took a less random approach, using BeAbo, a company recommended to me by a British tour operator.
Even with all the restaurant-specific sites on the Web, sometimes a random approach is best.
Possibly, they need to return to a more random approach.
In this approach, uncertainty is formulated by random variables with known forecasting information, and many possible scenarios are generated.
While the results above were obtained for one particular parameter set, we now consider the nonlinearity of the problem and the corresponding sensitivity to parameter variations by using an MC approach with 1000 random samples (Table 3).
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