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 vehicles will encounter situations where localization uncertainty is dominated by non-circular multipath effects.
If you go down this road far enough you eventually end up reinventing particle filters and similar.
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.
None of the inputs you mention work against multipath effects in cities, which means car GPS won't know which lane you're in and in a grid system may think you're on the next street over.
If you have an HD map you can solve for it using building shapes or by looking at the street with cameras. WiFi seems like it would help, but the locations of the WiFi terminals are themselves based on crowdsourced GPS.
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 all literal values, all this is is a "render" function. How can you reverse an arbitrary render function?
There are various approaches, depending on how Bayesian you want to be, but they boil down to: if your language supports redefining operators based on the types of the variables, and you have your variables contain a full specification of the subgraphs of computations that lead to them... you can create systems that can simultaneously do "forward passes" by rendering the relationships, and "backward passes" where the system can automatically calculate a gradient/derivative and thus allow a training system to "nudge" the likeliest values of variables in the right direction. By sampling these outputs, in a mathematically sound way, you get the weights that form a model.
Every layer in a deep neural network is specified in this way. Because of the composability of these operations, systems like PyTorch can compile incredibly optimal instructions for any combination of layers you can think of, just by specifying the forward-pass relationships.
So Uncertain<T> is just the tip of the iceberg. I'd recommend that everyone experiment with the idea that a numeric variable might be defined by metadata about its potential values at any given time, and that you can manipulate that metadata as easily as adding `a + b` in your favorite programming language.
If you're willing to be discrete about it, logic languages like Prolog and Mercury use "unification" instead of "evaluation" which means they can evaluate backwards.
https://colcarroll.github.io/ppl-api/ is likely a good starting point to get a taste of examples in Python; some use custom languages, but the success of Python-native frameworks in the LLM world I think has shown that embracing that makes interop and composability more possible at scale.
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.
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 could handle covariance.
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!
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
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.
If a test procedure is verifying all dimensional accuracy, it can be assumed to be bounding tolerance. If it's a mass production line with less than 100% testing of parts, you'd have to expect that some outliers get by and the tolerance is something like 3-sigma on a Gaussian.
Awesome! This speaks to something, which I've been thinking (and wishing) for a long time.
I've already done probabilistic programming in a scientific context (Python) and classical software engineering for web development (TypeScript, Python, Rust), and I've always wondered why I couldn't have the real-world modelling capacity of the former with the static type assurances of the latter. Love that you (and Microsoft) are thinking along the same lines!
Do you perhaps know of any Python implementations for this? There are plenty of dynamic stats programming libraries, but none offer typing solutions AFAIK.
This concept has been done many times in the past, under the name "interval arithmetic." Boost has it [1] as does flint [2]
What is really curious is why, after being reinvented so many times, it is not more mainstream. I would love to talk to people who have tried using it in production and then decided it was a bad idea (if they exist).
> Under the hood, Uncertain<T> models GPS uncertainty using a Rayleigh distribution.
And the Rayleigh distribution is clearly not just an interval with a uniformly random distribution in between. Normal interval arithmetic isn't useful because that uniform random distribution isn't at all a good model for the real world.
Take for example that Boost library you linked. Ask it to compute (-2,2)*(-2,2). It will give (-4,4). A more sensible result might be something like (-2.35, 2.35). The -4 lower bound is only attainable when you have -2 and 2 as the multiplicands which are at the extremes of the interval; probabilistically if we assume these are independent random variables then two of them achieving this extreme value simultaneously should have an even lower probability.
While it does sound like GP missed a distinction, I don't see how (-2.35, 2.35) would be sensible. The extremes can happen (or else they wouldn't be part of the input intervals) and the code has to sensibly deal with that event in order to be correct.
Using simple types (booleans etc) is very simple and easy to reason about, and any shortcomings are obvious. Trying to model physical uncertainty is difficult and requires different models for different domains. Once you have committed to needing to do that, it would be much better to use a purpose built model instead of a library which put some bell curves behind a pretty API.
I agree that different application strictly speaking need different models of uncertainty.
But I'm not so sure in your conclusion: a good enough model could be universally useful. See how everyone uses IEEE 754 floats, despite them giving effectively one very specific model of uncertainty. Most of the time this just works, and sometimes people have to explicitly work around floats' weirdnesses (whether that's because they carefully planned ahead because they know what they are doing, or whether they got a nasty surprise first). But overall they are still useful enough to be used almost universally.
Interval arithmetic is only a constant factor slower but may simplify at every step. For every operation over numbers there is a unique most precise equivalent op over intervals, because there's a Galois connection. But just because there is a most precise way to represent a set of numbers as an interval doesn't mean the representation is precise.
A computation graph which gets sampled like here is much slower but can be accurate. You don't need an abstract domain which loses precision at every step.
It would have been sort of interesting if we’d gone down the road of often using interval arithmetic. Constant factor slower, but also the operations are independent. So if it was the conventional way of handling non-integer numbers, I guess we’d have hardware acceleration by now to do it in parallel “for free.”
Several years ago when I discovered some of the historical work on interval arithmetic, I was astounded to find that there was a notable contingent in the 60s that was urging hardware developers to make interval arithmetic be the basic design of new CPUs, and saying quite forcefully that if we simply went with "normal" integers and floating point, we'd be unable to correctly model the world.
I think as another commenter pointed out, interval arithmetic’s problem is that while it acknowledges the reality of uncertainty, its model of uncertainty is so simplistic, in many applications it is unusable. So making it the standard primitive, could potentially result in the situation where apps that don’t need to explicitly model uncertainty at all have to pay the price of being forced to do so; meanwhile, apps which need a more realistic model of uncertainty are being forced to do so while being hamstrung by its interactions with another overly simple model. It is one of those ideas which sounds great in theory, but there are good reasons it never succeeded in practice-the space of use cases where explicitly modelling uncertainty is desirable, but where the simplistic model of interval arithmetic is entirely adequate, is rather small-a standard primitive which only addresses the needs of a narrow subset of use cases is not a good architecture
Interval arithmetic makes good intuitive sense when the endpoints of the intervals can be represented exactly. Figuring out how to do that, however, is not obvious.
Because reasoning about uncertain values / random variables / intervals / fuzzy logic / whatever is difficult and the model where things are certain is much easier to process while it models the reality well enough.
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.
A person might have mistyped a price, a barcode may have been misread, the unit prices might be correct but the quantity could be mistaken. Modeling uncertainty well isn't just about measurement error from sensors.
I wonder what it'd look like to propagate this kind of uncertainty around. You might want to check the user's input against a representative distribution to see if it's unusual and, depending on the cost of an error vs the friction of asking, double-check the input.
Money has the problem that no matter how clever you are someone will punch all the values into Excel and then complain they don't match.
Or specify they're paying X per day, but want hourly itemized billing...but it should definitely come out to X per day (this was one employer which meant I invoiced them with like 8 digits of precision due to how it divided, and they refused to accept a line item for mathematical uncertainty aggregates).
Yes, I worked in a billing department. No, floats are emphatically not suitable for representing money, except the very rounded values in presentations.
Floats try to keep the relative error at bay, so their absolute precision varies greatly. You need to sum them starting with the smallest magnitude, and do many other subtle tricks, to limit rounding errors.
I really like that this leans on computing probabilities instead of forcing everything into closed-form math or classical probability exercises. I’ve always found it way more intuitive to simulate, sample, and work directly with distributions. With a computer, it feels much more natural to uh... compute: you just run the process, look at the results, and reason from there.
If you are in an even more "approximate" mindset (as opposed to propagating by simulation to get real world re-sampled skewed distributions, as often happens in experimental physics labs, or at least their undergraduate courses), there is an error propagation (https://en.wikipedia.org/wiki/Propagation_of_uncertainty) simplification for "small" errors thing you can do. Then translating "root" errors to "downstream errors" is just simple chain rule calculus stuff. (There is a Nim library for that at https://github.com/SciNim/Measuremancer that I use at least every week or two - whenever I'm timing anything.)
It usually takes some "finesse" to get your data / measurements into territory where the errors are even small in the first place. So, I think it is probably better to do things like this Uncertain<T> for the kinds of long/fat/heavy tailed and oddly shaped distributions that occur in real world data { IF the expense doesn't get in your way some other way, that is, as per Senior Engineer in the article }.
Is there a risk that this will underemphasise some values when the source of error is not independent?
For example, the ROI on financial instruments may be inversely correlated to the risk of losing your job. If you associate errors with each, then combine them in a way which loses this relationship, there will be problems.
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.
This is still some code, as opposed to no code. It does seem to model everything in the research paper.
Aside from the original research paper needing to be included in the repo, it definitely does not need anything more than what's already there. It all builds and compiles without errors, only 2 warnings for the library proper and 6 warnings for the test project. Oh and it comes with a unit testing project: 59 tests written that covers about 73% of the library code. Only 2 tests failed.
Even having a unit testing library means it beats out like 50% of all repos you see on GitHub.
It was chosen to be implemented as a generic type in this design because the way that uncertainty "pollutes" underlying values maps well onto monads which were expressed through generics in this case.
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 vehicles will encounter situations where localization uncertainty is dominated by non-circular multipath effects.
If you go down this road far enough you eventually end up reinventing particle filters and similar.
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.
None of the inputs you mention work against multipath effects in cities, which means car GPS won't know which lane you're in and in a grid system may think you're on the next street over.
If you have an HD map you can solve for it using building shapes or by looking at the street with cameras. WiFi seems like it would help, but the locations of the WiFi terminals are themselves based on crowdsourced GPS.
As well as a fast fix because you know what mobile cell or wifi network you're on.
> Not to mention a fast fix because you can assume you haven't changed position since you last powered on.
... until you use a ferry.
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 all literal values, all this is is a "render" function. How can you reverse an arbitrary render function?
There are various approaches, depending on how Bayesian you want to be, but they boil down to: if your language supports redefining operators based on the types of the variables, and you have your variables contain a full specification of the subgraphs of computations that lead to them... you can create systems that can simultaneously do "forward passes" by rendering the relationships, and "backward passes" where the system can automatically calculate a gradient/derivative and thus allow a training system to "nudge" the likeliest values of variables in the right direction. By sampling these outputs, in a mathematically sound way, you get the weights that form a model.
Every layer in a deep neural network is specified in this way. Because of the composability of these operations, systems like PyTorch can compile incredibly optimal instructions for any combination of layers you can think of, just by specifying the forward-pass relationships.
So Uncertain<T> is just the tip of the iceberg. I'd recommend that everyone experiment with the idea that a numeric variable might be defined by metadata about its potential values at any given time, and that you can manipulate that metadata as easily as adding `a + b` in your favorite programming language.
Very interesting.
Are there PLs that support this kind of thing at the language level as you are describing?
If you're willing to be discrete about it, logic languages like Prolog and Mercury use "unification" instead of "evaluation" which means they can evaluate backwards.
https://colcarroll.github.io/ppl-api/ is likely a good starting point to get a taste of examples in Python; some use custom languages, but the success of Python-native frameworks in the LLM world I think has shown that embracing that makes interop and composability more possible at scale.
https://news.ycombinator.com/item?id=28941145 has some discussion here as well, though it’s a few years old.
Pyro and NumPyro seem to be popular at the moment!
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.
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 could handle covariance.
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!
If you need to track covariance you might want to play with gvar https://gvar.readthedocs.io/en/latest/ in python.
To properly model quantum mechanics, you’d have to associate a complex-valued wave function with any set of entangled variables you might have.
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
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.
If a test procedure is verifying all dimensional accuracy, it can be assumed to be bounding tolerance. If it's a mass production line with less than 100% testing of parts, you'd have to expect that some outliers get by and the tolerance is something like 3-sigma on a Gaussian.
Or for something likely relevant to many here - 3 point time estimates for project planning.
Probability distributions (even very simple ones) provide a much clearer view across any domain where there’s inherent uncertainty.
Awesome! This speaks to something, which I've been thinking (and wishing) for a long time. I've already done probabilistic programming in a scientific context (Python) and classical software engineering for web development (TypeScript, Python, Rust), and I've always wondered why I couldn't have the real-world modelling capacity of the former with the static type assurances of the latter. Love that you (and Microsoft) are thinking along the same lines! Do you perhaps know of any Python implementations for this? There are plenty of dynamic stats programming libraries, but none offer typing solutions AFAIK.
This concept has been done many times in the past, under the name "interval arithmetic." Boost has it [1] as does flint [2]
What is really curious is why, after being reinvented so many times, it is not more mainstream. I would love to talk to people who have tried using it in production and then decided it was a bad idea (if they exist).
[1]: https://www.boost.org/doc/libs/1_89_0/libs/numeric/interval/... [2]: https://arblib.org/
The article says,
> Under the hood, Uncertain<T> models GPS uncertainty using a Rayleigh distribution.
And the Rayleigh distribution is clearly not just an interval with a uniformly random distribution in between. Normal interval arithmetic isn't useful because that uniform random distribution isn't at all a good model for the real world.
Take for example that Boost library you linked. Ask it to compute (-2,2)*(-2,2). It will give (-4,4). A more sensible result might be something like (-2.35, 2.35). The -4 lower bound is only attainable when you have -2 and 2 as the multiplicands which are at the extremes of the interval; probabilistically if we assume these are independent random variables then two of them achieving this extreme value simultaneously should have an even lower probability.
While it does sound like GP missed a distinction, I don't see how (-2.35, 2.35) would be sensible. The extremes can happen (or else they wouldn't be part of the input intervals) and the code has to sensibly deal with that event in order to be correct.
Using simple types (booleans etc) is very simple and easy to reason about, and any shortcomings are obvious. Trying to model physical uncertainty is difficult and requires different models for different domains. Once you have committed to needing to do that, it would be much better to use a purpose built model instead of a library which put some bell curves behind a pretty API.
I agree that different application strictly speaking need different models of uncertainty.
But I'm not so sure in your conclusion: a good enough model could be universally useful. See how everyone uses IEEE 754 floats, despite them giving effectively one very specific model of uncertainty. Most of the time this just works, and sometimes people have to explicitly work around floats' weirdnesses (whether that's because they carefully planned ahead because they know what they are doing, or whether they got a nasty surprise first). But overall they are still useful enough to be used almost universally.
Interval arithmetic is only a constant factor slower but may simplify at every step. For every operation over numbers there is a unique most precise equivalent op over intervals, because there's a Galois connection. But just because there is a most precise way to represent a set of numbers as an interval doesn't mean the representation is precise.
A computation graph which gets sampled like here is much slower but can be accurate. You don't need an abstract domain which loses precision at every step.
It would have been sort of interesting if we’d gone down the road of often using interval arithmetic. Constant factor slower, but also the operations are independent. So if it was the conventional way of handling non-integer numbers, I guess we’d have hardware acceleration by now to do it in parallel “for free.”
You can probably get the parallelism for interval arithmetic today? Though it would probably require a bit of effort and not be completely free.
On the CPU you probably get implicit parallel execution with pipelines and re-ordering etc, and on the GPU you can set up something similar.
Not sure why this is being upvoted as the article is not describing interval arithmetic. It supports all kinds of uncertainty distributions.
Several years ago when I discovered some of the historical work on interval arithmetic, I was astounded to find that there was a notable contingent in the 60s that was urging hardware developers to make interval arithmetic be the basic design of new CPUs, and saying quite forcefully that if we simply went with "normal" integers and floating point, we'd be unable to correctly model the world.
I think as another commenter pointed out, interval arithmetic’s problem is that while it acknowledges the reality of uncertainty, its model of uncertainty is so simplistic, in many applications it is unusable. So making it the standard primitive, could potentially result in the situation where apps that don’t need to explicitly model uncertainty at all have to pay the price of being forced to do so; meanwhile, apps which need a more realistic model of uncertainty are being forced to do so while being hamstrung by its interactions with another overly simple model. It is one of those ideas which sounds great in theory, but there are good reasons it never succeeded in practice-the space of use cases where explicitly modelling uncertainty is desirable, but where the simplistic model of interval arithmetic is entirely adequate, is rather small-a standard primitive which only addresses the needs of a narrow subset of use cases is not a good architecture
Interval arithmetic makes good intuitive sense when the endpoints of the intervals can be represented exactly. Figuring out how to do that, however, is not obvious.
Also not all uncertainties are modeled well by uniform distributions over an interval.
Because reasoning about uncertain values / random variables / intervals / fuzzy logic / whatever is difficult and the model where things are certain is much easier to process while it models the reality well enough.
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.
Arguably Uncertain should be the default, and you should have to annotate a type as certain T when you are really certain. ;)
Only for physical measurements. For things like money, you should be pretty certain, often down to exact fractional cents.
It appears that a similar approach is implemented in some modern Fortran libraries.
> For things like money, you should be pretty certain, often down to exact fractional cents.
That's one way to look at it.
Another is that Money is certain only at the point of exchange.
> It appears that a similar approach is implemented in some modern Fortran libraries.
I'd be curious about that. Do you have a link?
A person might have mistyped a price, a barcode may have been misread, the unit prices might be correct but the quantity could be mistaken. Modeling uncertainty well isn't just about measurement error from sensors.
I wonder what it'd look like to propagate this kind of uncertainty around. You might want to check the user's input against a representative distribution to see if it's unusual and, depending on the cost of an error vs the friction of asking, double-check the input.
Typos seem like a different type of error from physical tolerances, and one that would be really hard to reason about mathematically.
Money has the problem that no matter how clever you are someone will punch all the values into Excel and then complain they don't match.
Or specify they're paying X per day, but want hourly itemized billing...but it should definitely come out to X per day (this was one employer which meant I invoiced them with like 8 digits of precision due to how it divided, and they refused to accept a line item for mathematical uncertainty aggregates).
have you ever tried working computationally with money? Forget money, have you worked with floating points? There really isn't anything certain.
Yes, I worked in a billing department. No, floats are emphatically not suitable for representing money, except the very rounded values in presentations.
Floats try to keep the relative error at bay, so their absolute precision varies greatly. You need to sum them starting with the smallest magnitude, and do many other subtle tricks, to limit rounding errors.
A complement to Optional.
I feel like if you're worried about picking the right abstraction then this is almost certainly the wrong one.
I really like that this leans on computing probabilities instead of forcing everything into closed-form math or classical probability exercises. I’ve always found it way more intuitive to simulate, sample, and work directly with distributions. With a computer, it feels much more natural to uh... compute: you just run the process, look at the results, and reason from there.
If you are in an even more "approximate" mindset (as opposed to propagating by simulation to get real world re-sampled skewed distributions, as often happens in experimental physics labs, or at least their undergraduate courses), there is an error propagation (https://en.wikipedia.org/wiki/Propagation_of_uncertainty) simplification for "small" errors thing you can do. Then translating "root" errors to "downstream errors" is just simple chain rule calculus stuff. (There is a Nim library for that at https://github.com/SciNim/Measuremancer that I use at least every week or two - whenever I'm timing anything.)
It usually takes some "finesse" to get your data / measurements into territory where the errors are even small in the first place. So, I think it is probably better to do things like this Uncertain<T> for the kinds of long/fat/heavy tailed and oddly shaped distributions that occur in real world data { IF the expense doesn't get in your way some other way, that is, as per Senior Engineer in the article }.
Is this essentially a programmatic version of fuzzy logic?
https://en.wikipedia.org/wiki/Fuzzy_logic
https://en.wikipedia.org/wiki/Probabilistic_programming more like. It is already a thing; see, for example, https://pyro.ai/
Always enjoy mattt’s work. Looks like a great library.
Is there a risk that this will underemphasise some values when the source of error is not independent? For example, the ROI on financial instruments may be inversely correlated to the risk of losing your job. If you associate errors with each, then combine them in a way which loses this relationship, there will be problems.
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/
Seems more proper to call it a `ProbabilityDistribution` type. It's a more general and intuitive way to handle the concept.
But the pun, uncertainty.
Yeah but the shorter name wins
oh man i had forgotten about this blog from when i orbited the swift ecosystem a bit... it's clearly as great as always! fun post!
Could this be implemented in Rust or Clojure?
Does Anglican kind of do this?
IS there the complete C# available for this? I looked over the original paper and it's just snippets.
https://github.com/klipto/uncertainty
10 years since commit and no attached documents besides a tiny readme. Pass.
Blame Microsoft Research, as the link came directly from them: https://www.microsoft.com/en-us/research/project/uncertainty.... I don't think they ever really took the project past the initial paper/presentation.
This is still some code, as opposed to no code. It does seem to model everything in the research paper.
Aside from the original research paper needing to be included in the repo, it definitely does not need anything more than what's already there. It all builds and compiles without errors, only 2 warnings for the library proper and 6 warnings for the test project. Oh and it comes with a unit testing project: 59 tests written that covers about 73% of the library code. Only 2 tests failed.
Even having a unit testing library means it beats out like 50% of all repos you see on GitHub.
S
[flagged]
> And why does it need to be part of the type system?
As presented in the article, it is indeed just a library.
It was chosen to be implemented as a generic type in this design because the way that uncertainty "pollutes" underlying values maps well onto monads which were expressed through generics in this case.
I don't think inference is part of this at all, frequentist or otherwise.
It's not part of the type system, it's just the giry monad as a library.
> 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.