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I misread this title (the word "confidence" threw me) and was initially very confused when it turned out to be about dimensional analysis instead of uncertainty.

But why not both? A number system with dimensions and which automatically propagates measurement uncertainty through calculations (a la Taylor's train wreck book) doesn't seem totally infeasible?

I would particularly like this for expressing cad models as code instead of brep.

▲ cosmic_quanta 4 days ago | parent [-]

There are Haskell packages for uncertainty (e.g. [0], based on automatic differentiation [1]). However, these packages don't support typed dimensions, because multiplication/division becomes more complex.

It is my goal to provide quantities with uncertainty AND typed dimensions one day.

[0]: https://hackage.haskell.org/package/uncertain

[1]: https://hackage.haskell.org/package/ad

▲ jcgrillo 4 days ago | parent | next [-]

Very cool. Thanks for the links, I'm not very familiar with the Haskell ecosystem, I mostly work in Rust and Go these days. I'll definitely check them out.

▲ ssivark 4 days ago | parent | prev [-]

This is the kind of thing that would be pretty straight-forward in Julia, I imagine. Independent libraries for uncertainties and units could easily be remixed together if the unitful quantity accepts any number type, and the uncertainties just define a new number type. Multiple dispatch should generate consistent downstream behavior "for free".

	▲	ziotom78 4 days ago \| parent [-]
		Indeed: `julia> using Unitful: m, s julia> using Measurements julia> Δx = (3.0 ± 0.1)m 3.0 ± 0.1 m julia> t = (1.0 ± 0.2)s 1.0 ± 0.2 s julia> v = Δx / t 3.0 ± 0.61 m s^-1`