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This allowed us to test whether the best-fitting decision rule we inferred can successfully replicate our data, and how the properties of decision making varied between species.
Besides statistical criteria supporting the decision on the best fitting model, content-related considerations and theoretical implications must also guide the decision process [40, 41].
Chi-square difference analyses guided decisions concerning selection of the best fitting model (i.e., the Satorra-Bentler scaled chi-square difference test which is appropriate for models estimated with MLR; [ 31]).
However, this uncertainty can be evaluated by significance tests; the discrimination between stable and unstable edges gives an additional basis of decision-making for the selection of the best fitting model.
The fuzzy decision rules are implemented as membership functions (MFs) and the model learns the best fitting parameters of the MFs.
Dotted lines represent the best fitting.
In the ultimate decision regarding the analysis and image processing selection, not only experience, but also the shape of the anomalies and the convergence of the best fitting model, are important criteria.
The best fitting was obtained with four sub-bands.
The best fitting is obtained at 14 ≤ n ≤ 22.
The best fitting for coatings was obtained using three fitting components with varied intensities but fixed positions and line widths.
In fact, one might observe that it was an entirely fitting decision.
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