Expressive
Turing models are easy to write and communicate, with syntax that is close to the mathematical specification of the model.
General-purpose
Turing supports models with discrete parameters and stochastic control flow.
Composable
Turing is written entirely in Julia, and is interoperable with its powerful ecosystem.
Start Your Journey
Whether you're new to Bayesian modeling or an experienced researcher, find the resources you need.
New to Turing?
Begin with the basics. Our step-by-step tutorials will guide you from installation to your first probabilistic models.
For Researchers
Dive into advanced models, explore the rich package ecosystem, and learn how to cite Turing.jl in your work.
For Developers
Join our community, contribute to the project on GitHub, and connect with fellow developers on Slack.
Supported by world-class institutions
Turing.jl is currently being developed at leading research organisations.
Turing.jl is an MIT Licensed Open Source Project
If you use Turing.jl in your research, please consider citing our papers.
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Fjelde, T. E., Xu, K., Widmann, D., Tarek, M., Pfiffer, C., Trapp, M., Axen, S. D., Sun, X., Hauru, M., Yong, P., Tebbutt, W., Ghahramani, Z., & Ge, H. (2025). Turing.jl: a general-purpose probabilistic programming language. ACM Transactions on Probabilistic Machine Learning. Just Accepted.
@article{Fjelde2025Turing, author = {Fjelde, Tor Erlend and Xu, Kai and Widmann, David and Tarek, Mohamed and Pfiffer, Cameron and Trapp, Martin and Axen, Seth D. and Sun, Xianda and Hauru, Markus and Yong, Penelope and Tebbutt, Will and Ghahramani, Zoubin and Ge, Hong}, title = {Turing.jl: a general-purpose probabilistic programming language}, journal = {ACM Transactions on Probabilistic Machine Learning}, year = {2025}, publisher = {Association for Computing Machinery}, doi = {10.1145/3711897}, note = {Just Accepted}, url = {https://doi.org/10.1145/3711897} } -
Ge, H., Xu, K., & Ghahramani, Z. (2018). Turing: a language for flexible probabilistic inference. In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS) (Vol. 84, pp. 1682-1690). PMLR.
@inproceedings{Ge2018Turing, author = {Ge, Hong and Xu, Kai and Ghahramani, Zoubin}, title = {Turing: a language for flexible probabilistic inference}, booktitle = {Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS)}, series = {Proceedings of Machine Learning Research}, volume = {84}, pages = {1682--1690}, year = {2018}, publisher = {PMLR}, url = {http://proceedings.mlr.press/v84/ge18b.html} }