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The advantage of model checking is to detect the failures during the design stage.
The main advantage of model trees is that, compared to neural networks, they represent understandable rules.
Another reason for the advantage of model adaptation may be due to the fundamental limitations of feature compensation.
The advantage of model checking based approaches is that several methods have been proposed for the reduction of the verification cost.
The main advantage of model trees, unlike the other soft computing tools, is their easier use and more importantly their understandable mathematical rules.
A key advantage of model linking is that it allows a wide range of real-world situations to be simulated from a small set of model components.
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As the prediction interval further increases, the advantage of Model-1 decreases, whereas the advantage of Model-2 increases.
A further advantage of model-based surveys is that they can be used with non-random strategies including balanced sampling.
The advantage of model-based approaches is that they allow the incorporation of prior knowledge acting as a regularisation that favours plausible solutions over implausible ones.
A main advantage of model-assisted estimation is that unbiasedness of estimators does not rely on the suitability of the model, but the model only helps to improve the precision of an estimator known to be (approximately) unbiased.
An advantage of model-assisted estimation, as compared to model-based and hybrid inference, is that the unbiasedness of estimators of totals and means do not rely on the correctness of the model; the model is only applied for enhancing a design-based estimator (Särndal et al. 1992).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com