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Re: Best random number generator algorithms to use with `gsl_ran_gaussia
Re: Best random number generator algorithms to use with `gsl_ran_gaussian`
Fri, 10 Dec 2021 13:43:57 -0800
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
Mersenne Twister is a good balance of speed and quality of the randomness (and
large period), while ranlxd2 makes no compromises for speed.
> On Dec 10, 2021, at 13:16, Vicent Giner Bosch <firstname.lastname@example.org> 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
> 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,