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Then, from the point of view of the parameter randomization, many researchers formulated and studied the stochastic epidemic models with randomized parameters (see [8 12] for example).
Recently, risk models with randomized dividend strategy have received a lot of attention in the literature.
The ANN models with randomized data stratification gave better predictions than those with fixed stratification.
To ensure that these results were not the consequence of spurious correlations, we trained GP models with randomized bioactivity values (y-scrambling) [60].
We determined the sensitivity of our LiDAR ACD model to the number of field calibration plots used by fitting simulated calibration models with randomized multiples of 4 plots (~ 5% of the data), from 4 to 76 plots (up to 90% of 83 total plots) [17].
We determined the sensitivity of our LiDAR ACD model to the number of field calibration plots used by fitting simulated calibration models with randomized multiples of 4 plots (~ 5% of the data), from 4 to 76 plots (up to 90% of 83 total plots) [ 17].
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However, for optimal results, the ANN model with randomized data stratification is preferred.
Albrecher et al. [12] study the expected discounted dividends in the compound Poisson model with randomized dividend decision times.
This also agrees with the R 2 results in Fig. 13 that the ANN model with randomized data stratification performed better than that with fixed stratification.
A two-lane traffic model with randomized turning points is also realized and the dynamical behavior has been investigated.
When compared with the results of Azad et al. (2010), the ANN model with randomized data stratification gave a C f -based prediction with up to 49%% improvement in correlation coefficient and 92 % error reduction.
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