Update docs/src/inverse_problems/structural_identifiability.md

Co-authored-by: Sam Isaacson <[email protected]>

Author: TorkelE

docs/src/inverse_problems/structural_identifiability.md

@@ -1,5 +1,7 @@
 # [Structural Identifiability Analysis](@id structural_identifiability)
-During parameter fitting, parameter values are inferred from data. Identifiability is a concept describing to what extent the identification of parameter values for a certain model is actually feasible. Ideally, parameter fitting should always be accompanied with an identifiability analysis of the problem. Identifiability can be divided into *structural* and *practical* identifiability[^1]. Structural identifiability considers only  the system and what quantities we can measure to determine which quantities can be identified. Practical identifiability instead considers the available data, and determines what system quantities can be inferred from it. Generally, in the hypothetical case of an infinite amounts of noise-less data, practical identifiability becomes identical to structural identifiability. Generally, structural identifiability is assessed before parameters are fitted, while practical identifiability is assessed afterwards.
+During parameter fitting, parameter values are inferred from data. Parameter identifiability refers to whether inferring parameter values for a given model is mathematically feasible. Ideally, parameter fitting should always be accompanied with an identifiability analysis of the problem. 
+
+Identifiability can be divided into *structural* and *practical* identifiability[^1]. Structural identifiability considers only the mathematical model, and which parameters are and are not inherently identifiable due to model structure. Practical identifiability also considers the available data, and determines what system quantities can be inferred from it. In the idealized case of an infinite amount of noise-less data, practical identifiability becomes identical to structural identifiability. Generally, structural identifiability is assessed before parameters are fitted, while practical identifiability is assessed afterwards.
 
 Structural identifiability (which is what this tutorial considers) can be illustrated by the following differential equation:
 ${dx \over dt} = p1*p2*x(t)$