Turing.jl is a universal probabilistic programming language embedded in Julia. Turing allows the user to write statistical models in 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. Since Turing is implemented in pure Julia code, its compiler and inference methods are amenable to hacking: new model families and inference methods can be easily added.
The Turing team is currently planning projects for GSoC 2025, and will post them here once they are ready. You are also welcome to propose your own projects. If you are interested in working on Turing for GSoC 2025, please reach out to Markus Hauru (@mhauru) on Julia's Slack or Discourse.