Turing.jl

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

Intuitive

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

Universal

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

Adaptable

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))
end

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.

Samplers

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

Community

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

GitHub

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

Slack

Discuss advanced topics. [Request access here](https://slackinvite.julialang.org/).

Go to Slack

Ecosystem

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

AdvancedHMC

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

Go to AdvancedHMC

MCMCChains

Chain types and utility functions for MCMC simulations.

Go to MCMCChains

Bijectors

Automatic transformations for constrained random variables.

Go to Bijectors