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.
Join our channel (
#turing) in the Julia Slack chat for help, discussion, or general communication with the Turing team. If you do not already have an invitation to Julia’s Slack, you can get one by going here.
If you use Turing for your own research, please consider citing the following publication: Hong Ge, Kai Xu, and Zoubin Ghahramani: Turing: Composable inference for probabilistic programming. AISTATS 2018 pdf bibtex