---
layout: splash
title: Turing.jl - Turing.jl
permalink: /
main-feature_row:
- title: "Intuitive"
excerpt: "Turing models are easy to read and write — models work the way you write them."
- title: "General-purpose"
excerpt: "Turing supports models with discrete parameters and stochastic control flow. Specify complex models quickly and easily."
- title: "Modular"
excerpt: "Turing is modular, written fully in Julia, and can be modified to suit your needs."
- title: "High-performance"
excerpt: "Turing is [fast](https://arxiv.org/abs/2002.02702)."
code-sample:
excerpt: "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."
url: "/docs/using-turing/quick-start"
snippet: |
@model function gdemo(x, y)
# Assumptions
σ² ~ InverseGamma(2,3)
μ ~ Normal(0,sqrt(σ²))
# Observations
x ~ Normal(μ, sqrt(σ²))
y ~ Normal(μ, sqrt(σ²))
end
samplers:
image_path: /assets/images/sampler.svg
excerpt: "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."
url: "/docs/library/#samplers"
flux:
image_path: /tutorials/figures/3_BayesNN_12_1.svg
excerpt: "Turing supports Julia's [Flux](http://fluxml.ai/) package for automatic differentiation. Combine Turing and Flux to construct probabilistic variants of traditional machine learning models."
url: "/tutorials/3-bayesnn"
ecosystem:
title: Ecosystem
subtitle: Explore a rich ecosystem of libraries, tools, and more to support development.
ecosystems:
- title: AdvancedHMC
url: https://github.com/TuringLang/AdvancedHMC.jl
text: Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms.
- title: MCMCChains
url: https://github.com/TuringLang/MCMCChains.jl
text: Chain types and utility functions for MCMC simulations.
- title: Bijectors
url: https://github.com/TuringLang/Bijectors.jl
text: Automatic transformations for constrained random variables.
community:
title: Community
subtitle: Join the Turing community to contribute, learn, and get your questions answered.
comunities:
- title: GitHub
url: https://github.com/TuringLang/Turing.jl
text: Report bugs, request features, discuss issues, and more.
class: github
- title: Turing.jl Discuss
url: https://discourse.julialang.org/c/domain/probprog
text: Browse and join discussions on Turing.
class: turing-resource
- title: Slack
url: https://julialang.slack.com/messages/turing/
text: Discuss advanced topics. [Request access here](https://slackinvite.julialang.org/).
class: slack
support:
title: Companies & Universities
Using Turing.jl
supports:
- image: assets/images/cambridge.png
text: Pushing the state of the art in probabilistic machine learning.
- image: assets/images/edinburgh.png
text: Using Turing’s flexibility to efficiently research new algorithmic approaches.
- image: assets/images/edinburgh.png
text: Educating the next wave of Data Scientists using Turing.
---
Bayesian inference with probabilistic programming.
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