This is how we get you ;-)
> On Oct 2, 2014, at 6:58 PM, Andrew Dolgert <adolg...@gmail.com> wrote: > > Darn, now it's on me. I've read the codebase, and could add the feature with > a little work. It's just a method on rand(), coupled with pulling code, such > as ziggurat, out of Base. > > Thanks, > Drew > >> On Wednesday, October 1, 2014 11:39:16 PM UTC-4, John Myles White wrote: >> Hi Andrew, >> >> It sounds like you've got a lot of interesting ideas for improving >> Distributions.jl. Please read through the existing codebase when you've got >> some time and submit pull requests for any functionality you'd like to see >> changed. >> >> In regard to your main question, I don't believe we support special RNG's in >> Distributions. >> >> -- John >> >>> On Oct 1, 2014, at 8:32 PM, Andrew Dolgert <adol...@gmail.com> wrote: >>> >>> It doesn't seem possible to use an explicit random number generator to >>> sample a distribution: >>> rng=MersenneTwister(seed) >>> rand(Distributions.Exponential(scale), rng) >>> Did I miss a way to do this? >>> >>> I want to use an explicit generator because >>> - I can serialize it and pick up where I left off with the next run >>> - I can use different generators in different parts of the program >>> - It's good hygiene for stochastic simulations to know when rand is used. >>> >>> Using quantile(distribution, rand(rng)) isn't great because it doesn't use >>> the accepted sampling algorithms. For instance, the ziggurat algorithm for >>> exponentials is far better than inverting the cdf. >>> >>> Thanks, >>> Drew >>
