I suspect you wouldn't have to test it all, as it would simply be a trivial redistribution of a standard (already known-good) generator: def random_bit_666(): return int(random() > 2/3) For other more sophisticated distributions, again simply verify that your distribution algorithm is correct and let some library function take care of the randomness. On Wed, Dec 7, 2022 at 8:23 AM David Lowry-Duda <david@lowryduda.com> wrote:
Inspired by the recent thread on PRNG, I began to wonder: suppose that I had a pseudorandom number generator that attempted to generate a nonuniform distribution. Suppose for instance that it was to generate a 0 bit 2/3 of the time, and a 1 bit 1/3 of the time.
How would one go about testing this PRNG against an idealized (similarly biased) PRNG?
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