Hi all,
I am thinking about contributing to Distributions.jl. Two questions:
Thanks,
Mattias
Welcome @matvil.
Great, thanks!
I agree with the more the better!
I just noticed that there’s no SkewNormal Skew normal distribution - Wikipedia for example.
Nice! skew-normal is a very useful distribution for real-world models.
This was my first post here at Julialang on Discord. Impressed by the very quick responses. Very encouraging!
Just to color this a bit, there’s a difference between “is the property known” and “did you include it in your PR”. If it is not known, then it’s fine that you didn’t include it. If it is known, then whether or not your PR should include it depends on how core it is to the utility of the distribution. If you don’t implement the kurtosis or the entropy, we can probably leave that for another day. If you leave out the mean when it’s known, we’d probably wait on a merge until you added it.
For example, Distributions does not currently include the alpha stable family (PRs welcome!). That family doesn’t have a known pdf, so that’s fine. But if your PR left out the characteristic function or the CMS sampler, I doubt we would merge it until you added them.
Yes, I read that between the lines, but good to clarify. Thanks.
While I think that Distributions.jl is the best place for most commonly used distributions, it would be nice to have facilitate packages defining distributions with a lightweight dependency, so I opened
If you’re going down this route, I’ll propose a huge amount of additonal work 
I often wonder if Distributions.jl would benefit from a refactor into UnsafeDistributions.jl that defines the types and does no error checking. That package could be wrapped with the current parameter checking to create Distributions.jl.
Unsure if people still complain about the error checking cost these days, but it was a theme of a few comments in the early days.
Can you please give an example of what kind of error checking you would skip?
Unclear if these are worth removing, but there’s still some checking logic that is slightly wasteful in a tight loop like this: https://github.com/JuliaStats/Distributions.jl/blob/20c91d9efcc5f96913bf8e38be2e3fb14b21942b/src/multivariate/dirichlet.jl#L34
I don’t see any of the heavier checks I thought existed in the past for MvNormal, but I might have misremembered those.
I would propose a uniform syntax to skip check, similar to Base.undef.
Eg a singleton Distributions.unsafe, as in
Dirichlet(unsafe, alpha)
as an inner constructor in addition.
I don’t have anything to add, but I have been idly wondering about this. When I look at the Turing stuff for example, I see a lot that makes me uneasy, but I think it ultimately reflects on the structure of Distributions. DistributionsAD.jl for instance includes line-for-line recoding of stuff from Distributions to get around certain conventions or inconsistencies about error checking or PDMats. And then AdvancedMH.jl introduces the idea of a “DensityModel” as the target for MH. I don’t know what this could mean other than a distribution, so it would at least be cute if you could just use Distribution subtypes there, but again I think the point is they’re getting around error checking and inconsistencies about what logpdf returns when you’re out of the support.
I like the idea of having a core set of distributions in Distributions.jl and other distributions packages using core functionalities from Distributions.jl. There are simply too many distributions out there, and Distributions.jl would quickly be overwhelmed, which is why asked my original question.
The unsafe suggestion is something I always wanted in Matlab (I used to define my own unsafe versions). Not sure if the overhead for safe checking is a big burden in Julia though.
Yes, I’ve reading the code again lately and there seems to be a lack of coherent strategy when to return 0/-Inf, NaN or throw an error. Would be great to clean up.
Perhaps a related question.
In my filed (HEP), one often (always) needs to use a customary PDFs.
A few years ago I was considering adding the function I need to Distributions:
Here is a propotype of my package AlgebraPDF.jl for creating customary PDFs and combinations of them, generating and fitting.
I am using it in my analysis, but have not found time yet to polish and publish. It certaintly has overlaps with the Distributions.jl. I am wondering if we can make use of it.
I’m a little confused about what’s going on.
using Distributions, Random, HypothesisTests;
Uni = subtypes(UnivariateDistribution)
#Cts_Uni = subtypes(ContinuousUnivariateDistribution)
DGP_True = LogNormal(17,7);
Random.seed!(123);
const d_train = rand(DGP_True, 1_000)
const d_test = rand(DGP_True, 1_000)
Er =[]; D_fit =[];
for d in Uni
println(d)
try
dd = "$(d)" |> Meta.parse |> eval
DĚ‚ = fit(dd, d_train)
Score = [loglikelihood(DĚ‚, d_test),
OneSampleADTest(d_test, DĚ‚) |> pvalue,
ApproximateOneSampleKSTest(d_test, DĚ‚) |> pvalue,
ExactOneSampleKSTest(d_test, DĚ‚) |> pvalue,
#PowerDivergenceTest(d_test,lambda=1) Not working!!!
JarqueBeraTest(d_test) |> pvalue #Only Normal
];
#Score = loglikelihood(DĚ‚, ds) #TODO: compute a better score.
push!(D_fit, [d, DĚ‚, Score])
catch e
println(e, d)
push!(Er, (d,e))
end
end
a=hcat(D_fit...)
M_names = a[1,:]; M_fit = a[2,:]; M_scores = a[3,:];
idx =sortperm(M_scores, rev=true);
Dfit_sort=hcat(M_names[idx], sort(M_scores, rev=true) )
julia> Dfit_sort
11Ă—3 Array{Any,2}:
LogNormal … [-20600.7, 0.823809, 0.789128, 0.781033, 0.0]
Gamma [-21159.4, 6.0e-7, 2.45426e-68, 1.23247e-69, 0.0]
Cauchy [-24823.3, 6.0e-7, 2.91142e-213, 8.6107e-227, 0.0]
InverseGaussian [-26918.1, 6.0e-7, 0.0, 0.0, 0.0]
Exponential [-33380.3, 6.0e-7, 0.0, 0.0, 0.0]
Normal … [-40611.5, 6.0e-7, 1.32495e-213, 3.51792e-227, 0.0]
Rayleigh [-61404.6, 6.0e-7, 0.0, 0.0, 0.0]
Laplace [-2.03419e9, 6.0e-7, 1.49234e-138, 5.47197e-144, 0.0]
DiscreteNonParametric [-Inf, 6.0e-7, 0.197933, 0.193494, 0.0]
Pareto [-Inf, 6.0e-7, 6.69184e-108, 3.7704e-111, 0.0]
Uniform … [-Inf, 6.0e-7, 0.0, 0.0, 0.0]
I’d like to better understand the advantage to keeping different distributions in different repos. The points I’ve heard are:
Back in 2013 @johnmyleswhite made a nice list of distributions to add to Distributions.jl: https://github.com/JuliaStats/Distributions.jl/issues/124
My biggest concern about having various distributions scattered throughout the Julia ecosystem: