Statistical and Randomness Testing

This group gathers numerical algorithms concerned with generating or testing randomness or computing a statistical estimate, including pseudorandom number generators such as the Mersenne Twister and linear congruential generators, sampling algorithms such as reservoir sampling and the Boltzmann sampler, approximate counting and streaming algorithms such as the Flajolet-Martin algorithm, randomness tests, Markov chain Monte Carlo methods such as the Swendsen-Wang algorithm, fractal dimension estimators such as the Higuchi dimension, and financial averaging statistics such as the volume-weighted and time-weighted average price. Their shared work is producing or evaluating a statistical or probabilistic estimate from data, as distinct from a deterministic numeric computation such as a matrix decomposition or a root-finding method.