AMDG Dima wrote:

Hi all, this is my first post here. As far as I've seen boost provides standard random number generators for each distribution class. So, if you want a normally distributed class, you're automatically using Box-Mueller.... However, there are other ways of generating random numbers, such as using the inverse cumulative distribution function and evaluate it at a uniformly generated random number.

It might be good to add this functionality to the library, since basically everything that is needed is there. Questions: Is this already in development? Is this something of interest? I'd be willing to participate somehow if this is not already in progress. Need first step guidance though.

The inverse cdf is often hard to calculate with sufficient accuracy. Some of the distributions, like the binomial and poisson distributions can be improved, but other algorithms are probably going to be more accurate and efficient. In Christ, Steven Watanabe