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Contextual additive modeling [45 48] assumes model parameters to be a sum of multiple independent components, each having different context dependencies; therefore, different decision trees have to be trained for them.
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When modelling absolute treatment effects across individuals, the assumed model usually has incorporated a variable benefit, but a fixed harm.
Under the assumed model, the cracks always pass the interface.
We derived the Best Linear Model Unbiased Estimator (BLMUE) of the population under the assumed model and response mechanism.
The assumed model was perfectly elastic, and no damping was required in the modeling with DFA.
When strict measurement invariance was assumed (Model 1), the estimated discrimination parameter was (c=1.55).
This is in accord with the commonly assumed model of the compatibilizer effect of block copolymers.
Enzymatic rate coefficients can then be obtained by fitting the observed MWDs to an assumed model.
Assuming Model (9), Table 6 shows the results for AR(1) and exchangeable correlation structures.
These results indicate that genetic evaluation and selection strategies will be sensitive to the assumed model.
Assuming model (1), the SNP effect was estimated by: β ^ 1 = x ′ x − x ′ y.
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