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.
Keywords for this software
References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Nicholson, George; Blangiardo, Marta; Briers, Mark; Diggle, Peter J.; Fjelde, Tor Erlend; Ge, Hong; Goudie, Robert J. B.; Jersakova, Radka; King, Ruairidh E.; Lehmann, Brieuc C. L.; Mallon, Ann-Marie; Padellini, Tullia; Teh, Yee Whye; Holmes, Chris; Richardson, Sylvia: Interoperability of statistical models in pandemic preparedness: principles and reality (2022)
- Suuronen, Jarkko; Chada, Neil K.; Roininen, Lassi: Cauchy Markov random field priors for Bayesian inversion (2022)
- Mathieu Besançon, Theodore Papamarkou, David Anthoff, Alex Arslan, Simon Byrne, Dahua Lin, John Pearson: Distributions.jl: Definition and Modeling of Probability Distributions in the JuliaStats Ecosystem (2021) not zbMATH
- Oliver Schulz, Frederik Beaujean, Allen Caldwell, Cornelius Grunwald, Vasyl Hafych, Kevin Kröninger, Salvatore La Cagnina, Lars Röhrig, Lolian Shtembari: BAT.jl - A Julia-based tool for Bayesian inference (2020) arXiv