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We checked for the number of models that satisfied the condition of very small intercept, negative coefficients, and positive profit and hit ratio.
Our findings were obtained from linear and parametric models that satisfied the assumption of errors normality.
The total number of models that satisfied the accuracy threshold criteria was 599, and the statistical characteristics of 15 most significant kNN models are shown in Table 3.
Random partitioning of the compounds into two classes (10 times) produced only three (for class 1) and 28 (for class 2) models that satisfied the criteria of q/ R > 0.5, compared with 1,207 and 40, respectively, models for "moving M-regression"–assisted partitioning.
For the modeling set with real HTS results, there were 599 models that satisfied the criterion of q /R > 0.65 (Table 3), whereas for the data set with randomized HTS results, only 5 models that had q /R > 0.65 were generated.
Through a pattern-matching algorithm, we selected the slip realisations from a large suite of randomised slip models that satisfied the given criteria on the spatial slip concentration (see the Appendix for further details).
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Then we propose some models that satisfy these constraints.
Design of experiments for identification of models that satisfy these inequalities is cumbersome.
Kolmogorov comments that infinite probability spaces are idealized models of real random processes, and that he limits himself arbitrarily to only those models that satisfy countable additivity.
From a stochastic perspective, we investigate the generation of Markov chain models that satisfy the observed target inhibition sensitivities.
The aims of the text are to present a methodology, BMA for input variables of energy models, that satisfies these requirements and to infer quantitative uncertainty estimations from input parameters to energy models.
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