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In contrast, Version 2 did not allow the user to modify the "look" of the screen, and also provided no opportunity for model selection: this feature was carried out optimally by the FDSS.
As the sample had already been used for model selection, this estimate of model performance is overly optimistic.
In Bayesian model selection, this constant is termed 'model evidence', while in parameter estimation problems, it is often referred to as the likelihood (of the unknown parameters) [27].
However if we were to consider model selection, this term would play a crucial role, and is thus given the special name of evidence.
Equipped with data set checkout plots, goodness of fit plots and tools for covariate model selection, this software has gained great popularity.
For computational efficiency in model selection, this study followed Yeung et al. [ 55] and used a deterministic search based on an Occam's window approach [ 54] and the "leaps and bounds" algorithm [ 56] to identify models with higher posterior probabilities.
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Based on this unrestricted model selection the model effects were tested for plausibility.
We extended this method to incorporate stepwise model selection in this paper.
However model selection for this particular purpose is still an ongoing research.
The data-driven method used for model selection in this paper found a reversible model for [18F]HX4.
First, as anticipated above, most of the works listed in Table 2 do not provide clear statements about model selection outcomes; this actually hinders the reproducibility of the experiments.
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