When random is not actually random enough

wilbo

I got lost when OP talked about using 10 integers to choose from 3 choices. I think I figured out what was missing in the explanation. random_u64() Mod 3 does indeed have a single bucket that is oversized. This overweights one option by about 5×10^-20. rand() Itself has only 32767 possible values, so it's also common for a bucket to be overweighted depending on the number of buckets.

tialaramex

The thing you actually want is rejection sampling: https://en.wikipedia.org/wiki/Rejection_sampling . That Wiki page makes it sound very complicated but for this purpose our implementation can be laughably simple which has the advantage that you know why it works and can maintain it properly with confidence. Get suitably large inputs, for example if you're trying to pick integers between 2 and 11 inclusive, a nibble (half a byte) would be fine. Now, is the random input in the range you wanted? If so, you've got your answer. If not, throw this random input away and get more. Too many programmers act as though random numbers were a precious resource.

fwlr

I don’t think the “random uint” api is too low-level, or lacks a pit of success - I think you’re just reaching for the wrong api. The problem of “make n bits pseudo randomly set to either 1 or 0” is nearby to your problem of “choose an element according to a probability distribution”, but it’s a separate problem in its own right. I think actually this is an argument for language designers to include a “std.choice” in their standard library that consumes random bytes and correctly performs common ergonomic operations like “get one element at random from this collection”. (If your standard library tries to make a distinction between “regular random number generators” and “cryptographically secured random number generators”, I think this distinction between “generate random bits” and “make probabilistic choices” is about equally important.)

westurner

Randomness test > Specific tests for randomness: https://en.wikipedia.org/wiki/Randomness_test Which NIST SP-800-22 implementation instead of the now-archived paranoid_crypto randomness tests? paranoid_crypto/docs/randomness_tests.md : https://github.com/google/paranoid_crypto/blob/main/docs/ran... /? NIST SP-800-22 Rust: https://www.google.com/search?q=NIST+SP-800-22+rust&oq=NIST+... Sometimes it's possible to whiten random to make it uniform random or normal random; Whitening transformation: https://en.wikipedia.org/wiki/Whitening_transformation

NooneAtAll3

so it's not really *random* that isn't random enough - it's the operator% that worsens it

soltanov

The modulo operator is not a uniform mapping; use Lemire's nearly divisionless method or simple rejection sampling and move on.

pmarreck

After finding out that trig/transcendental was basically not guaranteed to be equivalent across kernels (libc/musl), which caused the dreaded “only fails in CI” problem for me when I was trying to generate nonflat distributions of drng’s, I ended up creating github.com/pmarreck/random to solve it, which it did