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Bayesian inference with probabilistic programming
Expressive
Turing models are easy to write and communicate, with syntax that is close to the mathematical specification of the model.
General-purpose
Turing supports models with discrete parameters and stochastic control flow.
Composable
Turing is written entirely in Julia, and is interoperable with its powerful ecosystem.
Start Your Journey
Whether you’re new to Bayesian modeling or an experienced researcher, find the resources you need.
New to Turing?
Begin with the basics. Our step-by-step tutorials will guide you from installation to your first probabilistic models.
For Researchers
Dive into advanced models, explore the rich package ecosystem, and learn how to cite Turing.jl in your work.
For Developers
Join our community, contribute to the project on GitHub, and connect with fellow developers on Slack.
Core Packages
The Turing ecosystem is built on a foundation of powerful, composable packages.
DynamicPPL.jl
A domain-specific language and backend for probabilistic programming languages, used by Turing.jl.
JuliaBUGS.jl
A modern implementation of the BUGS probabilistic programming language in Julia.
TuringGLM.jl
Bayesian Generalized Linear models using @formula syntax and returns an instantiated Turing model.
AdvancedHMC.jl
A robust, modular and efficient implementation of advanced HMC algorithms. (abs, pdf)
News & Updates
Read the latest from the Turing team.
Turing.jl Newsletter 16
The fortnightly newsletter for the Turing.jl probabilistic programming language
Turing.jl Newsletter 15
The fortnightly newsletter for the Turing.jl probabilistic programming language
Turing.jl Newsletter 14
The fortnightly newsletter for the Turing.jl probabilistic programming language
Turing.jl Newsletter 13
The fortnightly newsletter for the Turing.jl probabilistic programming language
Turing.jl Newsletter 12
The fortnightly newsletter for the Turing.jl probabilistic programming language
GSoC Report for DoodleBUGS: a Browser-Based Graphical Interface for Drawing Probabilistic Graphical Models
Shravan Goswami's GSoC 2025 final report: goals, architecture, progress vs proposal, and how to try it.
Featured Tutorials
A selection of tutorials to get you started.
Get Started with Turing.jl
Our step-by-step tutorials will guide you from installation to your first probabilistic models.
Introduction: Coin Flipping
Learn the basic concepts of Bayesian modeling by working through a simple coin-flipping example.
Core Functionality
This article provides an overview of the core functionality in Turing.jl, which are likely to be used across a wide range of models.
Turing.jl is an MIT Licensed Open Source Project
If you use Turing.jl in your research, please consider citing our papers.