No choice of forecast model parameters can exactly match the "true" tumor, but the data assimilation system with one-phenotype model, Eq. (2), nevertheless provides good forecasts in the presence of a moderate degree of error and uncertainty in the model parameters.
However, due to variations of material properties, tasks to be executed, modeling errors, etc., the model parameters are not exactly known and given quantities.
Note that the adjective 'hidden' refers to the state sequence through which the model passes, not to the parameters of the model; even if the model parameters are known exactly, the model is still 'hidden'.
Its use for structure detection is a natural extension of this constrained minimisation approach to pseudolinear regression problems which produces some model parameters that are exactly zero and, therefore, yields a parsimonious system description.
When modelling a biochemical system, the fact that model parameters cannot be estimated exactly stimulates the definition of tests for checking unreliable estimates and design better experiments.
Thus, even when considering just 2 models, it proves impossible to determine exactly which model parameters are responsible for the disparity in model outcomes.
As with all modelling studies, the numerical results presented in this study depend on assumptions and model parameters which are not known exactly.
These correspond exactly to the latent variable IDR model parameters, e.g., ({ α k }, Pmax, rp, R0, and kout) in Eqs.
However, as the validity of the whole study depends on the model parameters chosen, several points need clarification including the rationale and motivation to choose exactly these parameters.
The application of the very efficient adjoint sensitivity analysis methodology (ASAM) for nonlinear systems is also illustrated by computing exactly the first-order sensitivities of selected facility responses to all model parameters.
