oh, addendum to the sidenote: widely-used algorithms are typically already implemented in mathematics packages, so understand them means just knowing their limitations. this will save you a lot of headaches.

On Thu, Jul 12, 2018 at 6:36 PM, nikita aigner <niki.aigner@gmail.com> wrote:
(it's been a while that i needed this...)
sidenote: if you want a proper gaussian, there are much better ways of generating one. the box-muller transform is one simple way: https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform but there are better ones i think. the way to do arbitrary continuous distributions is as far as i know inverse transform sampling (https://en.wikipedia.org/wiki/Inverse_transform_sampling) and for discrete there are algorithms based on subdividing a space.

generally, when you deal with random numbers, don't neglect the algorithms behind them. the artifacts from too short of a period in a random number generator or distributions that are off can lead to disastrous errors at least in physical modeling. this may of course not hold for other applications.

On Thu, Jul 12, 2018 at 5:54 PM, Antoine Schmitt <as@gratin.org> wrote:
I use randomness to simulate reality.

- I inject randomness at the lowest levels of a system to shake it from the inside, like your unconscious (or your brain) contains a lot of randomness
- I inject randomness at the outskirts of a system, to shake it from the outside, like you live in an (apparently) random world, though you manage to remain yourself, and it even nourishes you

no totally random

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