概率编程的贝叶斯推理。_Julia_下载.zip

# Turing.jl [](https://github.com/TuringLang/Turing.jl/actions?query=workflow%3ATuring-CI+branch%3Amaster) [](https://coveralls.io/github/TuringLang/Turing.jl?branch=master) [](https://codecov.io/gh/TuringLang/Turing.jl) [](https://turinglang.org/dev/docs/using-turing/) [](https://github.com/SciML/ColPrac) **Turing.jl** is a Julia library for general-purpose [probabilistic programming](https://en.wikipedia.org/wiki/Probabilistic_programming_language). Turing allows the user to write models using standard Julia syntax, and provides a wide range of sampling-based inference methods for solving problems across probabilistic machine learning, Bayesian statistics, and data science. Compared to other probabilistic programming languages, Turing has a special focus on modularity, and decouples the modelling language (i.e. the compiler) and inference methods. This modular design, together with the use of a high-level numerical language Julia, makes Turing particularly extensible: new model families and inference methods can be easily added. Current features include: - [General-purpose](https://turinglang.org/dev/tutorials/06-infinite-mixture-model/) probabilistic programming with an [intuitive modelling interface](https://turinglang.org/dev/tutorials/00-introduction/) - Robust, efficient [Hamiltonian Monte Carlo (HMC)](https://github.com/TuringLang/AdvancedHMC.jl) sampling for differentiable posterior distributions - [Particle MCMC](https://github.com/TuringLang/AdvancedPS.jl) sampling for complex posterior distributions involving discrete variables and stochastic control flows - Compositional inference via Gibbs sampling that combines particle MCMC, HMC, [Random-Walk MH (RWMH)](https://github.com/TuringLang/AdvancedMH.jl) and [Elliptical Slice Sampling](https://github.com/TuringLang/Turing.jl/blob/master/src/inference/ess.jl) - Advanced variational inference based on [ADVI](https://github.com/TuringLang/AdvancedVI.jl) and [Normalising Flows](https://github.com/TuringLang/Bijectors.jl) ## Getting Started Turing's home page, with links to everything you'll need to use Turing is: https://turinglang.org/dev/docs/using-turing/get-started ## What's changed recently? See [releases](https://github.com/TuringLang/Turing.jl/releases). ## Want to contribute? Turing was originally created and is now managed by Hong Ge. Current and past Turing team members include [Hong Ge](http://mlg.eng.cam.ac.uk/hong/), [Kai Xu](http://mlg.eng.cam.ac.uk/?portfolio=kai-xu), [Martin Trapp](http://martint.blog), [Mohamed Tarek](https://github.com/mohamed82008), [Cameron Pfiffer](https://business.uoregon.edu/faculty/cameron-pfiffer), [Tor Fjelde](http://retiredparkingguard.com/about.html). You can see the full list of on Github: https://github.com/TuringLang/Turing.jl/graphs/contributors. Turing is an open source project so if you feel you have some relevant skills and are interested in contributing then please do get in touch. See the [Contributing](https://turinglang.org/dev/docs/contributing/guide) page for details on the process. You can contribute by opening issues on Github or implementing things yourself and making a pull request. We would also appreciate example models written using Turing. ## Issues and Discussions Issues related to bugs and feature requests are welcome on the [issues page](https://github.com/TuringLang/Turing.jl/issues), while discussions and questions about statistical applications and theory should can place on the [Discussions page](https://github.com/TuringLang/Turing.jl/discussions) or [our channel](https://julialang.slack.com/messages/turing/) (`#turing`) in the Julia Slack chat. If you do not already have an invitation to Julia's Slack, you can get one by going [here](https://julialang.org/slack/). ## Related projects - The Stan language for probabilistic programming - [Stan.jl](https://github.com/StanJulia/Stan.jl) - Bare-bones implementation of robust dynamic Hamiltonian Monte Carlo methods - [DynamicHMC.jl](https://github.com/tpapp/DynamicHMC.jl) - Comparing performance and results of mcmc options using Julia - [MCMCBenchmarks.jl](https://github.com/StatisticalRethinkingJulia/MCMCBenchmarks.jl) ## Citing Turing.jl ## If you use **Turing** for your own research, please consider citing the following publication: Hong Ge, Kai Xu, and Zoubin Ghahramani: **Turing: a language for flexible probabilistic inference.** AISTATS 2018 [pdf](http://proceedings.mlr.press/v84/ge18b.html) [bibtex](https://github.com/TuringLang/Turing.jl/blob/master/CITATION.bib)