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Using our approach, different model alternatives can be compared, allowing effective analysis of the consequences of introducing changes to the models and identification of redundant model components that can be omitted without affecting the fit to measured data.
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aA recent critique of this approach and a discussion of more promising, model-based alternatives can be found in[17].
Results using simulated and empirical data show that the presence of dominant alternatives can bias model estimates, but by making scale a function of a smooth approximation of normalised minimum regret we can properly account for scale differences without the need to remove choice tasks with dominant alternatives from the dataset.
With our model alternative possibilities for improvement can be determined by what-if analyses.
In these models, each DMU or alternative can freely choose its own favorable system of weights to maximize its performance.
The first two moments of X∗, the overall completion time for this alternative model, can then be found using the same approach as for X.
We show that the regularization effects induced by the alternative model can be vastly different from those induced by Burgers viscosity depending on the smoothness of initial data in the limit of zero regularization.
An alternative model can simulate the network discovery process whereby the discovery in one region of the network will predispose the expansion of related discoveries.
Nonetheless, I don't think that an alternative model can really be ruled out in which the SM protein recognizes a distinct configuration of Sec17 molecules achieved only when they are bound to the 'cognate' SNARE complex.
For cases where exploratory models are evaluated, the choice of alternative models should also be clearly justified, since the number of alternative models can be large.
These alternative models can be compared using likelihood ratio tests to determine which model provides the best fit for the data.
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