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Preprint Number 1884
1884. Alexey Ovchinnikov, Anand Pillay, Gleb Pogudin, and Thomas Scanlon Multi-experiment parameter identifiability of ODEs and model theory E-mail: Submission date: 21 November 2020 Abstract: Structural identifiability is a property of an ODE model with
parameters that
allows for the parameters to be determined from continuous noise-free data.
This is natural prerequisite for practical identifiability. Conducting
multiple
independent experiments could make more parameters or functions of
parameters
identifiable, which is a desirable property to have. How many
experiments are
sufficient? In the present paper, we provide an algorithm to determine the
exact number of experiments for multi-experiment local identifiability and
obtain an upper bound that is off at most by one for the number of
experiments
for multi-experiment global identifiability. Mathematics Subject Classification: Keywords and phrases: |

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