Based on the descriptions in the docs, and since you don't care about speed of generation, I would recommend ranlxs2 for single-precision and ranlxd2 for double-precision floats.
Mersenne Twister is a good balance of speed and quality of the randomness (and large period), while ranlxd2 makes no compromises for speed. Jean-François > On Dec 10, 2021, at 13:16, Vicent Giner Bosch <vigi...@eio.upv.es> wrote: > > [CAUTION: Non-UBC Email] > > I am using GSL's `gsl_ran_gaussian` (actually, the version with unit > variance `gsl_ran_ugaussian`) to generate pseudorandom values of a > normal random variable. > > According to the documentation, one of the arguments of the function > `gsl_ran_gaussian` is a pseudorandom number generator `r` (an instance > of the `gsl_rng` struct) that will be intialised before calling > `gsl_ran_gaussian` for the first time. > > I was planning to use MT19937 (`gsl_rng_mt19937`) or one of the > RANLUX-like ones (`ranlxs0`, for instance) as the algorithm to be used > by `r`, but I am not sure. There are plenty of possible algorithms to > choose from (see > https://www.gnu.org/software/gsl/doc/html/rng.html#random-number-generator-algorithms). > > I am not looking for how fast the algorithm may be. I am rather much > more interested in generating pseudorandom values from a Gaussian > distribution with the best quality from a mathematical standpoint, > meaning that they seem as 'random' as possible. > > So, according to your experience, which is a good pseudorandom number > algorithm to be used when sampling from a Gaussian distribution. > > NOTE: In case it matters, those Gaussian observations will not be > combined in any way after being generated ---I mean, I am neither > thinking about adding or subtracting them nor creating n-tuples of > them, etc. > > I look forward to your answers, > > > -- > vicent > > @vginer_upv > vigibos.webs.upv.es >
