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Furthermore, if the model in Eq. (21) is fully observed, e.g., y1 =  x1,  y2 =  x2, then all parameters in the model are identifiable.

However, this approach may open discussions on the possibility of an overparameterization of models and the identifiability between the parameters of cured incidence and uncured survivals [ 10], although the parameters of the standard cure model are identifiable in some sense [ 19].

An aim of identifiability analysis is to determine if the parameters of a model are identifiable or not, i.e. whether its parameters can be uniquely estimated.

All parameters of this model are identifiable and hence have unique maximum likelihood estimates [ 19].

It means that all parameters in the model are identifiable and one dataset is enough to uniquely estimate them.

Despite the parameter dependencies, the posterior distributions of the ICRP and HMGU model are identifiable for all 16 investigations, this is, the investigation specific maximum a posteriori estimates are well defined and inferable (cf. Additional file 1 section 4.3).

A model is identifiable if the value of each model parameter can be uniquely determined under ideal conditions.

In such situations, it is hard to evaluate the accuracy of the learned models, because predictive accuracy does not guarantee that the values of latent variables are correct or that the model is identifiable.

These parameters for l were selected based on the intervals between samples, and guarantee that the model is identifiable when the biological noise and/or inter-individual difference components are included by ensuring that l is neither too short nor too long.

In this setting, the quasispecies model is identifiable.

This appendix shows that the full model is not statistically identifiable, whereas the reduced model is identifiable.