Turing is a universal probabilistic programming language with an intuitive modelling interface, composable probabilistic inference and computational scalability.

Turing provides Hamiltonian Monte Carlo (HMC) and particle MCMC sampling algorithms for complex posterior distributions (e.g. those involving discrete variables and stochastic control flows). 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 flow; and
  • Gibbs sampling that combines particle MCMC, HMC and many other MCMC algorithms.

Citing Turing

To cite Turing, please refer to the following paper. A sample BiBTeX entry entry is given below:

  title = 	 {{T}uring: a language for flexible probabilistic inference},
  author = 	 {Ge, Hong and Xu, Kai and Ghahramani, Zoubin},
  booktitle = 	 {Proceedings of the 21th International Conference on Artificial Intelligence and Statistics},
  year = 	 {2018},
  series = 	 {Proceedings of Machine Learning Research},
  publisher = 	 {PMLR},

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