Interesting read, thanks for linking. I'd be interested to see use cases where this actually provides a noticeably fairer outcome than the standard double-precision algorithm (which, as he mentions, has a relative error of 2^-53).

In LT the 2^-53 error has turned out to be good enough

In fact, with such an incredibly tiny error, I would be more concerned about the random source being well-behaved. My intuition says that, in order for that error to show up as a bias, the random

generator would have to be

*really* good (I'm sure you could quantify exactly what that means using the error bound -- e.g., how much could a uniform-bit-

generator diverge from perfect uniformity before the quantization error would be secondary to the

generator's uniformity error?)

Anyway, fun read.

Rejection sampling is the bomb. Sometimes a really expensive bomb, but the bomb nonetheless