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In particular, the transmission parameters form a practically identifiable combination and thus cannot be estimated separately, potentially leading to incorrect predictions of the effects of interventions.
We show how a recent method termed the structural correlation method (SCM) can be used for model reduction and for obtaining a set of practically identifiable parameters.
2.3.2 is discerning and thus able to provide a unique minimum, assuming that the constitutive law is practically identifiable.
Thus this provides a necessary condition for a practically identifiable model, i.e. even if this condition is satisfied, the model can be non-identifiable because of noisy data.
Since the correlation relationships now depend on U s) + x10 ≠ 0 and x20 ≠ 0, x30 ≠ 0, the parameters are practically identifiable.
We therefore define a sensitivity value to be practically identifiable for given input data if 95% of the distribution maintains a consistent sign, similar to definitions for parameter identifiability used in previous studies [ 7, 25].
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Among the Dinka tribe, family extends to practically every identifiable relative.
If more linearly independent ZREs can be constructed by adding new data sets with different inputs, the parameters are practically non-identifiable, otherwise they are structurally non-identifiable.
In general, non-identifiable parameters in a model can be classified into structurally and practically non-identifiable parameters.
On the contrary, if the non-identifiability can be remedied by data improvement, they are practically non-identifiable [ 14, 15].
In this situation, the parameters are practically non-identifiable.
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