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With the ever-increasing public availability of bioactivity data [12], it is possible to construct reliable target-prediction models using statistical or machine learning methods.
Finally, Blandon proposed the EVDF for six different constitutive models using statistical approaches and the equations are presented in Eq. (29).
A new procedure to reduce uncertainties in reservoir simulation models using statistical inference and observed data is presented in this paper.
However, it is important to compare the alternative models using statistical test and choose the appropriate models accordingly.
Weather forecast products are generated by postprocessing forecast output from several numerical weather prediction models using statistical approaches or local meteorologists' judgments.
These contrast images were then entered into second-level permutation-based random effects models using statistical nonparametric mapping (SnPM; Nichols and Holmes 2002; 10 000 permutations, 10-mm FWHM variance smoothing).
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The models use statistical analysis to identify stocks that have characteristics that, in the past, have been correlated with higher-than-average returns.
In this paper, we tried to unify the available data into a more general model using statistical analysis.
The proposed algorithm included 4756 datasets to validate the effectiveness of the CSA-LSSVM model using statistical criteria.
Atop that it builds a predictive model using statistical analyses of the impact of bad weather and upcoming events such as ball games and concerts.
It is possible to validate a model using statistical tests making it possible to account for the reliability of the model.
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models use statistical
template using statistical
sample using statistical
models using stable
models using genetic
models using city-specific
models using multilevel
models including statistical
models using new
models using multiple
models using multilinear
models using para-skilled
models using different
models using residual
models using logistic
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