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The Bayesian Information Criterion (BIC) [ 30] is also widely used in statistical applications for model selection purposes.
Moreover, the estimated marginal likelihoods are used for model selection purposes as we will see in next section.
By following the BF interpretation chart described in Jeffreys (1998), a BF ≥10 should be thought as strong evidence for the model M 1 over the model M 0. BFs were recently used for model selection purposes in the context of identifying alternative splicing events between biological samples (Katz et al., 2010).
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For model parameter selection purposes, the objective function needs to quantify the difference in generalization accuracy when varying the model parameter settings.
In this paper, the reverse flow reactor model selection for design purposes has been identified using a case-based reasoning tool capable of interactive querying and abstraction procedure.
These include surrogate model selection for the purpose of: (1) estimating the minimum of a deterministic function, (2) the design under uncertainty of a simple oscillator, and (3) the uncertainty quantification of a complex engineering system subject to a severe shock and vibration environment.
However model selection for this particular purpose is still an ongoing research.
These improvements in the FARMS implementation allow for making a better comparison of those methodologies and enable the user to change the model selection according to the purpose of the data analysis.
In our example, we selected the bases a-priori for illustrative purposes, but model selection is clearly more problematic in applied analyses.
For the purposes of model selection we removed these trials but included them in the final model on which the results are based.
Important steps in the procedure are: model purpose definition, model selection, data collection, data reconciliation, calibration of the model parameters and model unfalsification.
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