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docs/old_files/petab_ode_param_fitting.md

@@ -41,7 +41,7 @@ Generally, PEtab takes five different inputs to define an optimisation problem (
 4. **Experimental (or simulation) conditions**: The simulations (each corresponding to a potential experiment) carried out during each step of the optimisation process (a `Dict{String,Dict{Symbol,Float64}}`).
 5. **Measurements**: The measurements to which the model is fitted (a `DataFrame`). 
 
-### Observables
+### [Observables](@id petab_parameter_fitting_intro_observables)
 The observables define the quantities that we may measure in our experiments. Typically, each corresponds to a single species, however, [more complicated observables are possible](@ref petab_observables_observables). For each observable, we also need a noise formula, defining the uncertainty in its measurements. By default, PEtab assumes normally distributed noise, with a mean equal to the true value and a standard deviation which we have to define. It is also possible to use [more advanced noise formulas](@ref petab_observables_noise_formula).
 
 In our example, we only have a single possible observable, the `P` species. We will assume that the noise is normally distributed with a standard deviation `0.5` (in our case this is not true, however, typically the noise distribution is unknown and a guess must be made). We combine this information in a `PEtabObservable` struct (to access the `P` species we must use [`@unpack`](@ref simulation_structure_interfacing_symbolic_representation)). Finally, we store all our observables in a dictionary, giving each an id tag (which is later used in the measurements input).