# Distributions.jl using a custom random number generator

#1

Is there any way to use a distribution from Distributions.jl with a custom RNG?

I know how to sample in a reproducible manner from a uniform distribution (see x1 and x2 in the code below).
But Distrubutions.jl seems to rely on Base.Random.GLOBAL_RNG which I do not want to use

I read this post, but was unable to solve my issue

I found a Julia implementation for Binomial (in LightGraphs), which I might just use, but I would prefer to have this working for any distribution.

``````using RandomNumbers, Distributions

const RNG_XOR=Xorshifts.Xorshift1024Plus(0x1234567890abcdef)
const RNG_MERSENNE=MersenneTwister(2001)
const RNG_SELECTED=RNG_MERSENNE

x1=rand(RNG_XOR) #this works fine, x1 is reproducible each time I initialize my seed & custom RNG, I get the same value for x1
x2=rand(RNG_MERSENNE) #this works fine as well

# this line throws an error (I was hoping this would solve my problem...)
# Base.Random.GLOBAL_RNG=RNG_SELECTED

some_distr=Binomial(200,0.1)
rand(some_distr) #this 'value' is only reproducible if i run srand(some_seed) beforehand, but I want to use a different RNG
``````

#2

As I understand it, this isn’t currently possible for all distributions. Distributions.jl uses Rmath for various complicated functions, and so Julia RNG objects can’t be passed to control random number generation.

If this isn’t the case I’d love to hear it. Sampling from `Distributions.jl` with a specified RNG is a much-desired feature for me, and I think the reliance on Rmath is a (totally understandable) wart in the Julia ecosystem.

#3

Thank you. I saw that RMath was called.
Luckily Binomial, Bernoulli and Normal suffice for my use case (for now). Thus I can remain within Julia and rely on a custome RNG for those.