Abstract:One limiting assumption of many mathematical models for dynamic systems is that the parameters of the
system do not change during the observation period, which however does
not necessary hold in many cases.
This is typical for biological and medical systems, where we observe a high intra-individual variability in the model parameters. Hybrid time-varying parameter framework is able to capture the changes of parameters that may represent the change of state of the individual, for example
in HIV infected patients, changes of conditions in regulatory metabolic networks or diauxic bacterial growth on mixed sugar medium.
Thus, in these scenarios, a subset or even all the parameters have to be treated as time-varying in
order to capture the dynamics of the system.
An offline (batch) algorithm that combines particle filtering and the expectation maximization is
introduced for the identification of such systems.
The efficiency of the proposed method is illustrated through simulated and real-world examples.