Turing.jl is a Julia library for (universal) probabilistic programming. Current features include:
- Universal probabilistic programming with an intuitive modelling interface
- Hamiltonian Monte Carlo (HMC) sampling for differentiable posterior distributions
- Particle MCMC sampling for complex posterior distributions involving discrete variables and stochastic control flows
- Gibbs sampling that combines particle MCMC and HMC
Please visit Turing.jl wiki for documentation, tutorials (e.g. Get started) and other topics (e.g. Advanced usages).
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 Contribute wiki 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.
Turing was originally created and is now managed by Hong Ge. Current and past Turing team members include Hong Ge, Adam Scibior, Matej Balog, Zoubin Ghahramani, Kai Xu, Emma Smith, Emile Mathieu. You can see the full list of on Github: https://github.com/yebai/Turing.jl/graphs/contributors. Thanks for the important additions, fixes and comments.
To cite Turing, please refer to the technical report. Sample BibTeX entry is given below:
@ARTICLE{Turing2016,
author = {Ge, Hong and Xu, Kai and Scibior, Adam and Ghahramani, Zoubin and others},
title = "{The Turing language for probabilistic programming.}",
year = 2016,
month = jun
}