3 ms·

Can someone please give an example of how this could be used in practice? On the surface probability seems antithetical to the explicit well-defined determinis

by gitgud 5y ago

Can someone please give an example of how this could be used in practice? On the surface probability seems antithetical to the explicit well-defined determinism of programming.

iab 5y ago
I wouldn’t classify floating point computation as deterministic
- lossolo 5y ago
  Floating point computation is fully deterministic, every time you do computation on floating point it will give the same results for the same inputs. What you probably meant is that it's not compatible with humans decimal model of floating point math.
rsfern 5y ago
It’s super interesting for combining scientific models with modern statistical computation in a flexible way Also, randomized algorithms for all kinds of things are very useful in general-purpose computing
nuclearnice1 5y ago
Rather than focus on the non deterministic random sampling, frame it as a programming language or library for manipulating and fitting statistical distributions. An HTTP library is good for making web calls. A probabilistic program is good for estimating probabilities. One example would be polling. You could write down a program that estimates votes from political polls. Then feed it polling data and get estimates of how people will vote.
deleted 5y ago
[deleted]
infra6x 5y ago
You can generate a posterior distribution for any overdetermined problem. Basically, any scientific problem, as they must infer models inputs (parameteric or non-parametric) from observed model outputs (data). Determinism is a nice illusion that quickly breaks down on real data. Scientific problems are non-unique and noise or inadequate models cause data and model to not mesh with one another. Deterministic answers have the property of being precisely wrong as opposed to mostly correct.
xmprt 5y ago
The best example I've seen is the birthday paradox. How likely is it for 2 people in a N person class to have the same birthday? You can solve this deterministically using math pretty easily. But if you modify the question to ask how likely it is for 3 people, or you add things like February 29th and leap years, or you add the fact that births are more likely during summer months, then it becomes extremely difficult to solve this deterministically. Instead, you run a Monte Carlo simulation to get approximate probabilities. This is much simpler to code and can be easily modified to fit new conditions. A Monte Carlo simulation is probabilistic because you use random numbers in the simulation and you won't get a 100% perfect answer but you'll get close enough (and you can do some math to get error bounds).
- rambojazz 5y ago
  It will also be more time consuming to compute, won't it?

by gitgud 5y ago

Can someone please give an example of how this could be used in practice? On the surface probability seems antithetical to the explicit well-defined determinism of programming.

iab 5y ago

I wouldn’t classify floating point computation as deterministic

lossolo 5y ago
Floating point computation is fully deterministic, every time you do computation on floating point it will give the same results for the same inputs. What you probably meant is that it's not compatible with humans decimal model of floating point math.

rsfern 5y ago

It’s super interesting for combining scientific models with modern statistical computation in a flexible way Also, randomized algorithms for all kinds of things are very useful in general-purpose computing

nuclearnice1 5y ago

Rather than focus on the non deterministic random sampling, frame it as a programming language or library for manipulating and fitting statistical distributions. An HTTP library is good for making web calls. A probabilistic program is good for estimating probabilities. One example would be polling. You could write down a program that estimates votes from political polls. Then feed it polling data and get estimates of how people will vote.

deleted 5y ago

[deleted]

infra6x 5y ago

You can generate a posterior distribution for any overdetermined problem. Basically, any scientific problem, as they must infer models inputs (parameteric or non-parametric) from observed model outputs (data). Determinism is a nice illusion that quickly breaks down on real data. Scientific problems are non-unique and noise or inadequate models cause data and model to not mesh with one another. Deterministic answers have the property of being precisely wrong as opposed to mostly correct.

xmprt 5y ago

The best example I've seen is the birthday paradox. How likely is it for 2 people in a N person class to have the same birthday? You can solve this deterministically using math pretty easily. But if you modify the question to ask how likely it is for 3 people, or you add things like February 29th and leap years, or you add the fact that births are more likely during summer months, then it becomes extremely difficult to solve this deterministically. Instead, you run a Monte Carlo simulation to get approximate probabilities. This is much simpler to code and can be easily modified to fit new conditions. A Monte Carlo simulation is probabilistic because you use random numbers in the simulation and you won't get a 100% perfect answer but you'll get close enough (and you can do some math to get error bounds).

rambojazz 5y ago
It will also be more time consuming to compute, won't it?