Exact(4)
One of the questions that can be asked concerns the need of reducing models in order to combine them.
Our results indicate that this is an effective approach for comparing model alternatives and reducing models to the minimum complexity replicating measured data.
Our results indicate that the presented approach is effective for comparing model alternatives and reducing models to the minimum complexity replicating measured data.
By reducing models to a minimum number of parameters and equations, using detailed biophysical data we can reduce the number of free parameters that can then be efficiently fitted when these cellular models are embedded within whole organ models and fitted to compatible data.
Similar(56)
This is important, as growing complexity of the modelled systems requires reducing model complexity.
Measured raw data or image data are iteratively optimised based on the noise reducing model.
If sample size is fixed, approaches for reducing model complexity have to be used.
This comes at the expense of including possibly uncorrelated cancer types, diminishing signal strength and reducing model fit.
They compare two kinds of approach: reduced models and predictive value imputation.
This action is of utmost importance to confirm the adequacy of the final reduced models.
Reduced models of combined heat and power plants are required for different applications.
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