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Variograms were then built to capture the spatial variation in permeability between data points which was then used in the building of equiprobable realizations of the permeability distribution across the model using sequential Gaussian simulation (SGSIM).
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In this study, a feature-based simplification method of boundary representation (B-rep) models using sequential iterative volume decomposition is proposed.
Decay of Pt was modelled using sequential measurements of Pt levels in serum and urine combined with the administered dose of cisplatin, age, weight and height at the start of chemotherapy.
A multivariable logistic regression main-effects model was developed, using sequential backward elimination of non-significant variables (based on the model deviance statistic).
The hyperparameters in the probabilistic model are learned using sequential Monte Carlo (SMC) method, which is superior to standard Markov chain Monte Carlo (MCMC) methods for multi-modal distributions.
The source model was constructed using sequential strategy regarding global field power and residual variance [61], [62].
The most relevant features for each model were chosen using sequential forward floating selection (SFFS).
Two models were developed using sequential multiple regression to determine prediction of, social and emotional loneliness from those IVs having significant bivariate association with either DVs.
We estimate the three parameters in the Fisher-Kolmogorov model using a sequential approach.
Modelling detection probability curves on the observed distribution was done with the setting "Hazard key function with cosine series expansion" using sequential model selection based on the lowest Akaike Information Criterion.
In [22], we systematically solve this by using sequential model-based optimization.
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