https://github.com/turinglang/distributionsad.jl

Automatic differentiation of Distributions using Tracker, Zygote, ForwardDiff and ReverseDiff
https://github.com/turinglang/distributionsad.jl

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Automatic differentiation of Distributions using Tracker, Zygote, ForwardDiff and ReverseDiff

• Host: GitHub
• URL: https://github.com/turinglang/distributionsad.jl
• Owner: TuringLang
• License: mit
• Created: 2019-09-01T14:14:01.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2024-04-17T14:50:56.000Z (about 1 year ago)
• Last Synced: 2024-05-01T13:27:38.245Z (about 1 year ago)
• Language: Julia
• Homepage:
• Size: 492 KB
• Stars: 148
• Watchers: 10
• Forks: 28
• Open Issues: 28
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# DistributionsAD.jl

[![CI](https://github.com/TuringLang/DistributionsAD.jl/actions/workflows/CI.yml/badge.svg?branch=master)](https://github.com/TuringLang/DistributionsAD.jl/actions/workflows/CI.yml?query=branch%3Amaster)

This package defines the necessary functions to enable automatic differentiation (AD) of the `logpdf` function from [Distributions.jl](https://github.com/JuliaStats/Distributions.jl) using the packages [Tracker.jl](https://github.com/FluxML/Tracker.jl), [Zygote.jl](https://github.com/FluxML/Zygote.jl), [ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl) and [ReverseDiff.jl](https://github.com/JuliaDiff/ReverseDiff.jl). The goal of this package is to make the output of `logpdf` differentiable wrt all continuous parameters of a distribution as well as the random variable in the case of continuous distributions.

AD of `logpdf` is fully supported and tested for the following distributions wrt all combinations of continuous variables (distribution parameters and/or the random variable) and using all defined distribution constructors:

- Univariate discrete

    - `Bernoulli`

    - `BetaBinomial`

    - `Binomial`

    - `Categorical`

    - `Geometric`

    - `NegativeBinomial`

    - `Poisson`

    - `PoissonBinomial`

    - `Skellam`

- Univariate continuous

    - `Arcsine`

    - `Beta`

    - `BetaPrime`

    - `Biweight`

    - `Cauchy`

    - `Chi`

    - `Chisq`

    - `Cosine`

    - `Distributions.AffineDistribution`

    - `Epanechnikov`

    - `Erlang`

    - `Exponential`

    - `FDist`

    - `Frechet`

    - `Gamma`

    - `GeneralizedExtremeValue`

    - `GeneralizedPareto`

    - `Gumbel`

    - `InverseGamma`

    - `InverseGaussian`

    - `Kolmogorov`

    - `Laplace`

    - `Levy`

    - `Logistic`

    - `LogitNormal`

    - `LogNormal`

    - `Normal`

    - `NormalCanon`

    - `NormalInverseGaussian`

    - `Pareto`

    - `PGeneralizedGaussian`

    - `Rayleigh`

    - `Semicircle`

    - `SymTriangularDist`

    - `TDist`

    - `TriangularDist`

    - `Triweight`

    - `Uniform`

    - `Weibull`

- Multivariate continuous

    - `MvLogNormal`

    - `MvNormal`

- Matrix-variate continuous

    - `MatrixBeta`

    - `Wishart`

    - `InverseWishart`

# Get Involved

A number of distributions are still either broken or not fully supported for various reasons. See [this issue](https://github.com/TuringLang/DistributionsAD.jl/issues/2). If you can fix any of the broken ones, a PR is welcome!