# TuringLang/Turing.jl

Bayesian inference with probabilistic programming.

Repository: https://github.com/TuringLang/Turing.jl
Canonical: https://ross.abutalabs.com/products/turingjl
Homepage: https://turinglang.org
Language: Julia
License: MIT
License Family: permissive
Topics: machine-learning, probabilistic-programming, julia-language, artificial-intelligence, bayesian-inference, hamiltonian-monte-carlo, turing, bayesian-statistics, mcmc, hmc, probabilistic-graphical-models, probabilistic-models, probabilistic-inference, bayesian-neural-networks
Last push: 2026-08-26T21:26:02+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 86, longevity 100
- inputs: {"age_days": 3778, "days_push": 7, "days_rel": 12, "gap_med": 5.0, "n_releases_24m": 69}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2251, forks 242 (observed 2026-08-28T04:06:31.018309+00:00)

## What it is
Turing.jl is a probabilistic programming library in Julia for Bayesian inference. Models are defined with an @model macro resembling mathematical notation, and inference is performed via MCMC samplers such as NUTS/HMC.

## Use cases
- fit bayesian models with mcmc in julia
- perform hamiltonian monte carlo inference on probabilistic models
- define statistical models with a math-like dsl
- do bayesian linear regression with posterior sampling
- model hierarchical and probabilistic graphical models
- run bayesian neural network inference
- sample from posteriors with discrete and continuous parameters

## When to choose
- you work in Julia and want expressive probabilistic programming with MCMC
- you need general-purpose Bayesian inference including discrete parameters and stochastic control flow
- you want model syntax close to mathematical specification
- you need interoperability with the Julia ecosystem and multiple AD backends

## When to avoid
- you need a non-Julia language like Python or R (consider Stan, PyMC, or NumPyro)
- you need fast VI-only workflows or heavy GPU deep-learning training rather than Bayesian inference
- you require commercial-grade support with guaranteed response times

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, simulation, data-science
- domain: machine-learning, artificial-intelligence, data-science
- platform: windows, cross-platform
- tags: probabilistic-programming, bayesian-inference, mcmc, hamiltonian-monte-carlo, bayesian-statistics, statistical-modeling, algorithms, julia, linux, macos

## Member repositories
- TuringLang/Turing.jl (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:31.018309+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:43:50.126378+00:00, confidence not recorded.
  - readme: https://github.com/TuringLang/Turing.jl (fetched 2026-08-28T04:06:31.018309+00:00, sha 34f916975189)
  - homepage: https://turinglang.org (fetched 2026-08-29T10:23:57.407354+00:00, sha 6941401a26a9)
  - site_page: https://turinglang.org/docs/getting-started (fetched 2026-08-29T10:23:57.416683+00:00, sha 4bb602d0707c)
  - site_page: https://turinglang.org/docs/tutorials (fetched 2026-08-29T10:23:57.418605+00:00, sha 89d2b034d958)
  - site_page: https://turinglang.org/docs/faq (fetched 2026-08-29T10:23:57.420168+00:00, sha b8f64f33e5cb)
  - site_page: https://turinglang.org/docs/tutorials/coin-flipping (fetched 2026-08-29T10:23:57.421811+00:00, sha b73d55c0e571)
  - site_page: https://turinglang.org/docs/core-functionality (fetched 2026-08-29T10:23:57.423453+00:00, sha edd9508ca2e6)
- Data as of 2026-08-30T08:39:29.467469+00:00.