Hi there, I need to use the cdf (and quantile) function of the T distribution inside a function that I want to use Automatic Differentiation on. This works fine with almost any univariate distribution from the Distributions.jl package, but unfortunately not with the T distribution. Here is an MWE: …

Anyone having an idea of what I could do? I am afraid the Github link is out of my league.

Finite differences are always available as workaround?

Perhaps using HypergeometricFunctions.jl would be acceptable? I doubt this is as fast or robust as some other options, but in easy tests it seems to be fine: using StatsFuns, SpecialFunctions, HypergeometricFunctions, ForwardDiff, FiniteDifferences # not thoughtfully written at all function tcdf(v…

Don’t these packages address this problem? [image] GitHub - tpapp/LogDensityProblems.jl: A common framework for implementing and... A common framework for implementing and using log densities for inference. - GitHub - tpapp/LogDensityProblems.jl: A common framework for implementing and …

Thanks a lot for the suggestions and your work! I checked the manual implementation of the cdf with HypergeometricFunctions and it does work for my example! Unfortunately, it seems to segfault for quite a wide range of parameter values, making it difficult to work with. I adjusted my expectations …

Thanks for your reply! DistributionsAD does indeed do that, but there does not seem to be a method defined for the cdf of the Tdistribution.

Or also there add tdist cdfs and quantiles by oscardssmith · Pull Request #147 · JuliaStats/StatsFuns.jl · GitHub there is a PR fixing this issue properly (implementing the right functions into StatsFuns.jl). It might just need some help to comply to the reviews and fix it properly, but this is a go…

T Distribution - Automatic Differentiation on quantile and cdf function

General Usage

mrVeng November 7, 2022, 3:42pm 10

Thats great news, thanks for the link!

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T Distribution - Automatic Differentiation on quantile and cdf function

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