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Uncertain

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Re: Uncertain<T>

#11

Post by jakubmazanec »

geocar wrote: post #7
jakubmazanec wrote: post #4

[flagged]

> What if I want Bayesian? Bayes is mentioned on page 46. > And why does it need to be part of the type system? It could be just a library. It is a library that defines a type. It is not a new type system, or an extension to any particularly complicated type system. > Am I missing something? Did you read it? https://www.microsoft.com/en-us/research/wp-content/uploads/... https://github.com/klipto/Uncertainty/

> Bayes is mentioned on page 46.

Bayes isn't mentioned in the linked article. But thanks for the links.

Re: Uncertain<T>

#12

Post by layer8 »

boscillator wrote: post #3

Does this handle covariance between different variables? For example, the location of the object your measuring your distance to presumably also has some error in it's position, which may be correlated with your position (if, for example, if it comes from another GPS operating at a similar time). Certainly a univarient model in the type system could be useful, but it would be extra powerful (and more correct) if it c…

To properly model quantum mechanics, you’d have to associate a complex-valued wave function with any set of entangled variables you might have.

Re: Uncertain<T>

#14

Post by krukah »

Monads are really undefeated. This particular application feels to me akin to wavefunction evolution? Density matrices as probability monads over Hilbert space, with unitary evolution as bind, measurement/collapse as pure/return. I guess everything just seems to rhyme under a category theory lens.

Re: Uncertain<T>

#17

Post by mikepurvis »

AlotOfReading wrote: post #8

A small note, but GPS is only well-approximated by a circular uncertainty in specific conditions, usually open sky and long-time fixes. The full uncertainty model is much more complicated, hence the profusion of ways to measure error. This becomes important in many of the same situations that would lead you to stop treating the fix as a point location in the first place. To give a concrete example, autonomous vehicle…

Vehicle GPS is usually augmented by a lot of additional sensors and assumptions, notably the speedometer, compass, and knowledge the you'll be on one of the roads marked on its map. Not to mention a fast fix because you can assume you haven't changed position since you last powered on.

Re: Uncertain<T>

#18

Post by valcron1000 »

krukah wrote: post #14

Monads are really undefeated. This particular application feels to me akin to wavefunction evolution? Density matrices as probability monads over Hilbert space, with unitary evolution as bind, measurement/collapse as pure/return. I guess everything just seems to rhyme under a category theory lens.

Relevant (2006): https://web.engr.oregonstate.edu/~erwig/pfp/

Re: Uncertain<T>

#19

Post by evanb »

boscillator wrote: post #3

Does this handle covariance between different variables? For example, the location of the object your measuring your distance to presumably also has some error in it's position, which may be correlated with your position (if, for example, if it comes from another GPS operating at a similar time). Certainly a univarient model in the type system could be useful, but it would be extra powerful (and more correct) if it c…

If you need to track covariance you might want to play with gvar https://gvar.readthedocs.io/en/latest/ in python.

Re: Uncertain<T>

#20

Post by 8note »

for mechanical engineering drawings to communicate with machinists and the like, we use tolerances

eg. 10cm +8mm/-3mm

for what the acceptable range is, both bigger and smaller.

id expect something like "are we there yet" referencing GPS should understand the direction of the error and what directions of uncertainty are better or worse

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