Two modelling techniques were used, both appropriate for skewed data: Poisson and negative binomial regression.
The full platform uses two modelling techniques: agent-based modelling (Castiglione 2006) and particle swarm optimisation (PSO) (del Valle et al. 2008).
These two modelling techniques combined interpretability and performance, as shown through the odds ratio table and tree diagram, which can be easily followed by a clinician during the diagnostic process.
Two modelling methods, Discrete Phase Modelling (DPM) and Effective Property Modelling (EPM) have been employed for numerical study to model nanofluids as working fluid in micro flow paths and the DPM predictions have been observed to match accurately with experiments.
Two modelling approaches (the turbulent mixer model of Baldyga and the spectral relaxation model of Fox) are compared with experimental data of a laboratory tubular jet reactor.
Two modelling methods were considered, the Poisson Regression Model (PRM) and the Negative Binomial Model (NBM).
Two modelling approaches were used: M3 code (hydrochemical mixing and mass balance model), developed by the Swedish Nuclear Fuel and Waste Management Company SKBB) and HYTEC (reactive transport model) developed by Ecole des Mines de Paris.
Two modelling paradigms were applied to the prediction of phytoplankton abundance in the Burrinjuck Reservoir: the deductive model SALMO and the inductive model ANNA.
Two modelling methods have been used; cohort simulation and individual-level simulation.
Two modelling approaches are used one with an analytical basis using classical plate theory and one with a numerical basis using the FEM to generate the design charts.
Two modelling methods have been examined where the first is by use of fixed fixed eigenmodes for the innermost substructure and fixed free eigenmodes for the outermost substructure.
