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This is because all the models involve unknown parameters or functions, which should be estimated from observations of the process.
Such dynamical models involve unknown parameters, such as kinetic parameters, that one must guess from prior knowledge or estimate from experimental data in order to analyze and simulate the model.
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Dynamical models used in systems biology involve unknown kinetic parameters.
Some triplets involve "unknown" atoms ("X").
Distributed parameter models involve far fewer unknown parameters than independent modal characteristics or finite element models.
Identification of parametric nonlinear models involves estimating unknown parameters and detecting its underlying structure.
We consider a class of nonlinear diffusion and reaction models involving an unknown parameter, e.g. activation energy in the case of classical thermal combustion phenomena[1], or the Thiele modulus in the case of enzyme kinetics[2].
Many models involve more than one unknown.
Do these models involve controllable probabilistic dependence?
Of course, if a model involves more unknown functions, it is difficult to obtain the asymptotic behavior of traveling wave solutions by the inequalities similar to (3.6) and (3.7).
Using complex segregation analysis a two-locus model involving an unknown major locus and a second locus that lies within or close to the swine leukocyte antigen (SLA) complex jointly determine risk of SSCM in pedigreed animals.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com