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A robust, efficient and modular library for general-purpose probabilistic AI.


Turing models are easy to read and write. Specify models quickly and easily.


Turing supports models with stochastic control flow — models work the way you write them.


Turing is written fully in Julia, and can be modified to suit your needs.

A Quick Example

Turing’s modelling syntax allows you to specify a model quickly and easily. Straightforward models can be expressed in the same way as complex, hierarchical models with stochastic control flow.

Quick Start

 @model gdemo(x, y) = begin
  # Assumptions
  σ ~ InverseGamma(2,3)
  μ ~ Normal(0,sqrt(σ))
  # Observations
  x ~ Normal(μ, sqrt(σ))
  y ~ Normal(μ, sqrt(σ))

Large Sampling Library

Turing provides Hamiltonian Monte Carlo sampling for differentiable posterior distributions, Particle MCMC sampling for complex posterior distributions involving discrete variables and stochastic control flow, and Gibbs sampling which combines particle MCMC, HMC and many other MCMC algorithms.


Integrates With Other Machine Learning Packages

Turing supports Julia’s Flux package for automatic differentiation. Combine Turing and Flux to construct probabalistic variants of traditional machine learning models.

Bayesian Neural Network Tutorial

Bayesian Neural Network Tutorial


Join the Turing community to contribute, learn, and get your questions answered.


Report bugs, request features, discuss issues, and more.

Go to GitHub

Turing.jl Discuss

Browse and join discussions on Turing.

Go to Turing.jl Discuss


Discuss advanced topics. [Request access here](

Go to Slack


Explore a rich ecosystem of libraries, tools, and more to support development.


Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms.

Go to AdvancedHMC


Chain types and utility functions for MCMC simulations.

Go to MCMCChains


Automatic transformations for constrained random variables.

Go to Bijectors