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MRMs provide a unifying framework for an integrated specification of model structure and system requirements.
It should be noted that structural identifiability assumes an ideal context of error-free model structure and noise-free measurements.
Detailed model structure and the software implementation are described.
both model structure and parameter errors.
We describe a systematic approach to evaluating differences in model assumptions and results, as well as differences in modeling culture underlying the differences in model structure and parameters.
This includes studying existing sampling algorithms; their relative efficiencies in relationship to hierarchical model structure; and potential modifications to existing sampling algorithms, including hybrid designs.
Predictions are always subject to uncertainty arising from two sources: model structure and training data.
Finally the problem of the overall model structure and organization is addressed.
Often the parameters are not uniquely identifiable for a given model structure and measurement set.
Among these methods, MGGP possesses the ability to evolve the model structure and its coefficients automatically.
Selectivity improvement results from model structure and nonlinear rates of reaction.
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