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Uncertain

nshipster.com

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Re: Uncertain<T>

#71

Post by akst »

Something I've wanted to make was a data type to represent a value that may or may not be known with a level of certainty over a certain distribution (or probability density function), but you could apply various transforms that may or may not have their own level of uncertainty, and you end up with a refined set of probability distributions each observation (or a new set of classifications based on whatever conditionals).

With the eventual goal of running various simulations over different randomly generated outcomes based on those probability distributions.

Re: Uncertain<T>

#72

Post by joerick »

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…

I've been wondering for a while if a program could "learn" covariance somehow. Through real-world usage.

Otherwise, it feels to me that it'd be consistently wrong to model the variables as independent. And any program of notable size is gonna be far too big to consider correlations between all the variables.

As for how one might do the learning, I don't know yet!

Re: Uncertain<T>

#73

Post by nullhole »

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…

Lidar points aren't points, they're spheroids centred on the most likely location

Re: Uncertain<T>

#74

Post by internet_points »

black_knight wrote: post #23

This seems closely related to this classic Functional Pearl: https://web.engr.oregonstate.edu/~erwig/papers/PFP_JFP06.pdf It’s so cool! I always start my introductory course on Haskell with a demo of the Monty Hall problem with the probability monad and using rationals to get the exact probability of winning using the two strategies as a fraction.

See also the Haskell library monad-bayes https://monad-bayes.netlify.app/tutorials/ https://www.tweag.io/blog/2019-09-20-monad-bayes-1/

Re: Uncertain<T>

#75

Post by mabster »

taneq wrote: post #44
mabster wrote: post #40

Earlier quoted context omitted.

Something that's bugged me about this notation though is that sometimes it means "cannot exceed the bounds" and sometimes it means "only exceeds the bounds 10% of the time"

I don’t think I’ve ever seen mechanical drawings have “90% confidence” dimensions like this. If a part’s too big then it won’t fit, and it’s probably useless.

Yeah it's probably field specific and I guess Gaussian-based uncertainty would be more about statistical sampling rather than tolerances. I've noticed that if arithmetic is being done on it it's almost certainly Gaussian. I just mean whenever I see uncertainty like this, I don't know what is meant!

Re: Uncertain<T>

#76

Post by danhau »

btown wrote: post #56

Once one understands that a variable (in a programming context) can hold a specification for a variable (in a mathematical context), one opens up incredible doors that are at the foundation of modern AI. When you see y = m * x + b, your recollections of math class may note that you can easily solve for "m" or find a regression for "m" and "b" given various data points. But from a programming perspective, if these are…

This sounds super interesting, but as someone who knows little about ML or math in general, could you give an ELI5?

Re: Uncertain<T>

#77

Post by jeffreygoesto »

mikepurvis wrote: post #17
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.

Well. Some part of the 101 was moved a bunch of feet sideways after construction. Really hard to correct for, the GPS and the map localization were constantly fighting like an old couple... Had to re-map that stretch quickly...

Re: Uncertain<T>

#78

Post by MangoToupe »

btown wrote: post #56

Once one understands that a variable (in a programming context) can hold a specification for a variable (in a mathematical context), one opens up incredible doors that are at the foundation of modern AI. When you see y = m * x + b, your recollections of math class may note that you can easily solve for "m" or find a regression for "m" and "b" given various data points. But from a programming perspective, if these are…

This comment seems to conflate variables, functions, and linear systems. I don't think these are worth conflating.

Re: Uncertain<T>

#79

Post by geocar »

jakubmazanec wrote: post #11
geocar wrote: post #7

Earlier quoted context omitted.

> 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.

That did not surprise me because I did not think the article was about anything but about adapting the dot-net library they linked to on Microsoft's site to swift, and I figured that if I wanted to understand the library and the approach I had better read the links that indicated I might be able to learn from them.

Re: Uncertain<T>

#80

Post by blauditore »

mikepurvis wrote: post #17
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.

> assume you haven't changed position since you last powered on

Sounds like a classic case of programmers ignoring corner cases: Towing, ferries, car trains, pushing the car because it broke down...

It's when you find messages in the log like "this should never happen".

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