On the identifiability and distinguishability of nonlinear parametric models Original Research Article

Testing parametric models for identifiability and distinguishability is important when the parameters to be estimated have a physical meaning or when the model is to be used to reconstruct physically meaningful state variables that cannot be measured directly. Examples are used to explain why and indicate briefly how, with special emphasis on nonlinear models.