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Model comparison is realized by applying two procedures.
A second approach to model comparison is to penalize model complexity (models that have more parameters).
We show that this Bayesian model comparison is just a natural formalization of Occam׳s razor.
Model comparison is a difficult task since models are generated using various subsets or various chemical compound descriptors.
The model comparison is centered on a well defined task, the Iowa Gambling Task, used in several psychological experiments.
Model comparison is based on the quality of the simulated geophysical data from the ensemble of conditional realizations.
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Fittings and model comparison were done using the nlme package of the statistical software R [18].
Implementation of these models and computation of some criteria for model comparison were illustrated using two simulated datasets.
Model comparison was based on deviance information criterion (DIC) (Spiegelhalter et al. 2002), in which the model with the lowest DIC value is considered the best.
Model comparison was made using Akaike's Information Criterion AICC) to evaluate which predictors were more closely associated with the outcome [33, 34].
In Gómez et al. [85], a model comparison was also done to show the relevance of each feature in every dataset.
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