hello all,
I am new to Julia. I need to generate random numbers from power law, broken power law and different distributions. Is there a package that do all of these? Thank you very much in advance.
Welcome to the discussion board.
I have not used this, but perhaps Distributions.jl might work for you?
More precisely, here https://juliastats.org/Distributions.jl/stable/univariate/#Sampling(Randomnumbergeneration) you can find the univariate distributions and sampling from an arbitrary one.
Example:
using Distributions, Plots
plotlyjs()
const α = 2.75
const xmin= 3.0
d = Pareto(α, xmin)
smpl= rand(d, 1000)
h=histogram(smpl, bins= xmin:50, normalize=:pdf,
framestyle=:box, size=(500, 250), legend=false)
Broken power law distribution is not implemented in Distributions.jl. It depends on the number of break points and power indices on each interval.
I only know of the existence of the broken power law, implemented in astropy: https://docs.astropy.org/en/stable/_modules/astropy/modeling/powerlaws.html, but without a sampling method.
Don’t know much about broken power laws, but mixture models might be able to piece them together:
alpha_below = 1.6
alpha_above = 1.2
breakpoint = 5.4
p_below = cdf(Pareto(alpha_below), breakpoint)
broken_dist = MixtureModel([truncated(Pareto(alpha_below), nothing, breakpoint),
truncated(Pareto(alpha_above), breakpoint, nothing)],
[p_below, 1  p_below])
rand(broken_dist)
If your distribution is known and sufficiently smooth^{[1]} you can use ApproxFun.jl.

Though in practice 2M Chebyshev modes is enough to approximate some very wonky functions. ↩︎
I implemented brokenpowerlaw sampling in InitialMassFunctions.jl, which is now a registered package. This functionality is provided by the BrokenPowerLaw
type. This type implements the Distributions.jl sampler
API, so a more efficient object for sampling can be constructed via Distributions.sampler(::BrokenPowerLaw)
. You may also be interested in the single powerlaw PowerLawIMF
type, which is just a wrapper on top of the Pareto distribution from Distributions.jl.
All of this is really helpful. Thanks a lot!