Turing.jl
Bayesian inference with probabilistic programming
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Expressive
Turing models are easy to write and communicate — their syntax closely resembles mathematical notation.
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.
Hello, World in Turing

Some text about how easy it is to get going.

@model function coinflip(; N::Int)
    # Prior belief about the probability of heads
    p ~ Beta(1, 1)

    # Heads or tails of a coin are drawn from `N`
    # Bernoulli distributions with success rate `p`
    y ~ filldist(Bernoulli(p), N)

end;
Goodbye, World in Turing

Some text about how easy it is to interface with external packages like AbstractGPs. Learn more about modelling Gaussian Processes with Turing.jl.

@model function putting_model(d, n; jitter=1e-4)
    v ~ Gamma(2, 1)
    l ~ Gamma(4, 1)
    f = GP(v * with_lengthscale(SEKernel(), l))
    f_latent ~ f(d, jitter)
    binomials = Binomial.(n, logistic.(f_latent))
    y ~ product_distribution(binomials)
    return (fx=f(d, jitter), f_latent=f_latent, y=y)
end

News

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Ecosystem

Learn about all the packages that are part of the TuringLang organization.

Documentation

Familiarize yourself with the mechanics of Turing.jl.

Official tutorials

Learn to tackle specific modelling problems with Turing.jl.

Algorithms

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Supported MCMC and other learning algorithms
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Entry A Entry D Entry G Entry J
Entry B Entry E Entry H Entry K
Entry C Entry F Entry I Entry L

Bayesian Workflow

Placeholder text introducing the Bayesian Workflow diagram from the ACM special issue submission.

An example of the Beeysian workflow.

Talks

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  • Talk 1
  • Talk 2
  • Talk 3

Other resources

This list contains a few other written resources for learning Turing. If you have written something and would like to share it with the community, please get in touch!

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