Methods to simulate a Normal distribution doesn’t really help you simulate a measured distribution of data, which was the stated problem, wasn’t it? The parent

Methods to simulate a Normal distribution doesn’t really help you simulate a measured distribution of data, which was the stated problem, wasn’t it? The parent comment’s suggestion to use the tabulated Inverse Transform method is already likely to be the optimal choice for simulating a discrete data distribution. There’s one alternative called the Alias Method that is fast to sample, but uses a discontinuous mapping so it compromises your ability to benefit from stratified or Quasi-Random sampling, which means it loses significant efficiency for Monte Carlo applications and thus doesn’t get used very often. (This is true of Ziggurat as well, and aside from Ziggurat the SO page you linked only has two other decent suggestions and one of them is the Inverse Transform method. Box-Muller is historically interesting, but I think doesn’t really “compete”.) So, anyway, FWIW, it doesn’t really seem super nuanced. I’m guessing the article’s author was just writing for a non-technical audience and isn’t clearly stating the problems being investigated by the COINFLIPS team.