Turing.jl is a Julia library for general-purpose probabilistic programming. Turing allows the user to write models using standard Julia syntax, and provides a wide range of sampling-based inference methods for solving problems across probabilistic machine learning, Bayesian statistics, and data science. Compared to other probabilistic programming languages, Turing has a special focus on modularity, and decouples the modelling language (i.e. the compiler) and inference methods. This modular design, together with the use of a high-level numerical language Julia, makes Turing particularly extensible: new model families and inference methods can be easily added. Current features include: General-purpose probabilistic programming with an intuitive modelling interface Robust, efficient Hamiltonian Monte Carlo (HMC) sampling for differentiable posterior distributions Particle MCMC sampling for complex posterior distributions involving discrete variables and stochastic control flows Compositional inference via Gibbs sampling that combines particle MCMC, HMC, Random-Walk MH (RWMH) and Elliptical Slice Sampling Advanced variational inference based on ADVI and Normalising Flows Getting Started Turing's home page, with links to everything you'll need to use Turing is: https://turing.ml/dev/docs/using-turing/get-started Full description in GitHub: https://github.com/TuringLang/Turing.jl/tree/v0.24.0 The title and description of this software/code correspond with the situation when the software metadata was imported to ACRIS. The most recent version of metadata is available in the original repository.

2022