Title: A PrioriIdentifiability of Physiological Parametric Models
Abstract: A fundamental question in parametric model identification is a priori global identifiability: whether or not, under ideal conditions of noise-free observations and error-free model structure, the unknown parameters of the postulated model can be estimated from the designed multi-input/multi-output experiment. The answer to this question is a necessary prerequisite for well-posedness of parameter estimation. Although necessary, a priori idenfiability is not sufficient to guarantee successful parameter estimation from real data (a posteriori or numerical identifiability) or, even more, model validity. In fact, a priori identifiable model can be rejected for several reasons, such as that it cannot explain the data or the precision with which its parameters can be estimated is poor due to a structure too complex for the data, or the paucity of the data. However, these aspects should not detract from satisfying the a priori identifiability requirement for any model used to obtain parameter values of interest.
Publication Year: 2001
Publication Date: 2001-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
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Cited By Count: 4
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