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We present our own interpretation and application of the model using simpler terminology (original model terminology is placed in brackets).
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A model using simple variables of routine measurement at ICU was predictive for failed extubation in our setting; it has good calibration and acceptable discrimination.
To predict tie persistence we build a classification model using simple logistic regression (LogR) models where the positive class is tie persistence, that is, that we observe at least a communication event in (Omega_{mathrm{after}}).
In 2008, a new hypothesis revised this model using simple game theory.
Our model, using simple mathematical equations featuring a small number of variables, is an attempt to quantitatively and prospectively interpret V/W.
The decay of unused catabolic functions, found experimentally, is introduced in the model using simple population genetics.
We have produced a demand-driven staffing model using simple spreadsheet technology, based on treatment protocols for HIV-positive patients that adhere to Mozambican guidelines.
We produced a demand-driven staffing model using simple spreadsheet technology, based on treatment protocols for HIV-positive patients that adhere to Mozambican guidelines.
The second important advantage of hierarchical Bayesian modeling is that it is simple to specify large, complex models using simpler statements about conditionally independent parameters.
This model uses simple effectiveness equations for steady-state heat and mass exchangers within a finite difference procedure.
Additionally, the proposed model uses simple arithmetic operations rather than traditional fuzzy GERT with applications in complex manufacturing systems.
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