# Documentation

**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:

```
@InProceedings{turing17,
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},
}
```