# Parameter estimation with DifferentialEquations.jl for a subset of parameters

**URL:** <https://discourse.julialang.org/t/parameter-estimation-with-differentialequations-jl-for-a-subset-of-parameters/33851>\
**Category:** Optimization (Mathematical)\
**Tags:** diffeq, optim, optimization\
**Created:** [January 27, 2020, 2:40pm UTC](https://discourse.julialang.org/t/parameter-estimation-with-differentialequations-jl-for-a-subset-of-parameters/33851 "2020-01-27T14:40:39Z")\
**Posts on this page:** 1\
**Showing post:** 3

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**Author:** ![parb](https://avatars.discourse-cdn.com/v4/letter/p/4da419/32.png) [@parb](https://discourse.julialang.org/u/parb)\
**Post date:** [March 3, 2022, 8:22am UTC](https://discourse.julialang.org/t/parameter-estimation-with-differentialequations-jl-for-a-subset-of-parameters/33851/3 "2022-03-03T08:22:05Z")

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I’ve used such an approach, but would love it if you’ve reworked this. Any progress?

In my case, I’ve got many (~50) parameters, three of which I estimate (with Turing.jl), and I’d love to be able to use a NamedTuple for the parameters, perhaps one for the fixed and one for the estimated parameters.

Edit: Found Chris’ advice on another topic\* to use closures\*\*. I’m not much of a programmer, so didn’t know what this was, but looked it up and its everything I needed. I was searching for a long time on how to handle these ~50 named parameters in a safe way, and think closures are the solution.

- [Pass optional parameters to model in DifferentialEquations.jl](https://discourse.julialang.org/t/pass-optional-parameters-to-model-in-differentialequations-jl/50718)
- [Julia Language Tutorial =\> Introduction to Closures](https://riptutorial.com/julia-lang/example/23207/introduction-to-closures)

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