Naively, I'd assume that if you give me a biased RNG with a known distribution, I'd think I could just unroll the distribution back to something uniform.
This needn't even involve division per se. A simple lookup table should suffice.
Of course, the meeaning isn't that people aren't to work on crypto but that people shouldn't create RNGs like this thinking they are better than the prior-art, when they actually regress with already solved probems.
This is the exact different between arc4random() and arc4random_uniform().
arc4random_uniform() is recommended over constructions like
arc4random() % upper_bound as it avoids "modulo bias" when
the upper bound is not a power of two.
We can use the C function rand() for this:
int i = rand()%10+1; def uneven_random():
# return 0 with 2/7 probability, 1 with 2/7 probability, and 2 with 3/7 probability
n = random.random()
if n < 2.0/7: return 0
if n < 4.0/7: return 1
return 2
def transformed_random():
# return 0 or 1 with 50% probability
N = uneven_random()
while N == 2 : N = uneven_random()
return N