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
