This chapter is aimed at helping readers decide whether identifiability and the closely connected property of distinguishability are theoretically important and practically relevant for their research or teaching. If this is so, they will find here methods that can be used to test models for these properties. The chapter also shows that measures of identifiability can be maximized, provided that there are some degrees of freedom in the procedure for data collection. If the model of interest cannot be made identifiable, all may not be lost, as we shall see. A large part of the presentation is tutorial in nature, with academic examples worked out in detail.


Parameter Vector Inclusion Function Fisher Information Matrix Outer Approximation Optimal Experiment Design 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.Laboratoire des Signaux et SystèmesCNRS–SUPELEC–Univ Paris-SudGif-sur-YvetteFrance

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