api.md

API

Module-wide re-exports

Turing.jl directly re-exports the entire public API of the following packages:

• Distributions.jl
• MCMCChains.jl

Please see the individual packages for their documentation.

Individual exports and re-exports

In this API documentation, for the sake of clarity, we have listed the module that actually defines each of the exported symbols. Note, however, that all of the following symbols are exported unqualified by Turing. That means, for example, you can just write

using Turing

@model function my_model() end

sample(my_model(), Prior(), 100)

instead of

DynamicPPL.@model function my_model() end

sample(my_model(), Turing.Inference.Prior(), 100)

even though Prior() is actually defined in the Turing.Inference module and @model in the DynamicPPL package.

Modelling

Exported symbol	Documentation	Description
@model	DynamicPPL.@model	Define a probabilistic model
@varname	AbstractPPL.@varname	Generate a VarName from a Julia expression
to_submodel	DynamicPPL.to_submodel	Define a submodel
prefix	DynamicPPL.prefix	Prefix all variable names in a model with a given VarName
LogDensityFunction	DynamicPPL.LogDensityFunction	A struct containing all information about how to evaluate a model. Mostly for advanced users

Inference

Exported symbol	Documentation	Description
sample	StatsBase.sample	Sample from a model
MCMCThreads	AbstractMCMC.MCMCThreads	Run MCMC using multiple threads
MCMCDistributed	AbstractMCMC.MCMCDistributed	Run MCMC using multiple processes
MCMCSerial	AbstractMCMC.MCMCSerial	Run MCMC using without parallelism

Samplers

Exported symbol	Documentation	Description
Prior	Turing.Inference.Prior	Sample from the prior distribution
MH	Turing.Inference.MH	Metropolis–Hastings
Emcee	Turing.Inference.Emcee	Affine-invariant ensemble sampler
ESS	Turing.Inference.ESS	Elliptical slice sampling
Gibbs	Turing.Inference.Gibbs	Gibbs sampling
HMC	Turing.Inference.HMC	Hamiltonian Monte Carlo
SGLD	Turing.Inference.SGLD	Stochastic gradient Langevin dynamics
SGHMC	Turing.Inference.SGHMC	Stochastic gradient Hamiltonian Monte Carlo
PolynomialStepsize	Turing.Inference.PolynomialStepsize	Returns a function which generates polynomially decaying step sizes
HMCDA	Turing.Inference.HMCDA	Hamiltonian Monte Carlo with dual averaging
NUTS	Turing.Inference.NUTS	No-U-Turn Sampler
IS	Turing.Inference.IS	Importance sampling
SMC	Turing.Inference.SMC	Sequential Monte Carlo
PG	Turing.Inference.PG	Particle Gibbs
CSMC	Turing.Inference.CSMC	The same as PG
RepeatSampler	Turing.Inference.RepeatSampler	A sampler that runs multiple times on the same variable
externalsampler	Turing.Inference.externalsampler	Wrap an external sampler for use in Turing

Variational inference

See the variational inference tutorial for a walkthrough on how to use these.

Exported symbol	Documentation	Description
vi	AdvancedVI.vi	Perform variational inference
ADVI	AdvancedVI.ADVI	Construct an instance of a VI algorithm

Automatic differentiation types

These are used to specify the automatic differentiation backend to use. See the AD guide for more information.

Exported symbol	Documentation	Description
AutoForwardDiff	ADTypes.AutoForwardDiff	ForwardDiff.jl backend
AutoReverseDiff	ADTypes.AutoReverseDiff	ReverseDiff.jl backend
AutoMooncake	ADTypes.AutoMooncake	Mooncake.jl backend

Debugging

setprogress!

Distributions

These distributions are defined in Turing.jl, but not in Distributions.jl.

Flat
FlatPos
BinomialLogit
OrderedLogistic
LogPoisson

Tools to work with distributions

Exported symbol	Documentation	Description
I	LinearAlgebra.I	Identity matrix
filldist	DistributionsAD.filldist	Create a product distribution from a distribution and integers
arraydist	DistributionsAD.arraydist	Create a product distribution from an array of distributions
NamedDist	DynamicPPL.NamedDist	A distribution that carries the name of the variable

Predictions

Exported symbol	Documentation	Description
predict	StatsAPI.predict	Generate samples from posterior predictive distribution

Querying model probabilities and quantities

Please see the generated quantities and probability interface guides for more information.

Exported symbol	Documentation	Description
returned	DynamicPPL.returned	Calculate additional quantities defined in a model
pointwise_loglikelihoods	DynamicPPL.pointwise_loglikelihoods	Compute log likelihoods for each sample in a chain
logprior	DynamicPPL.logprior	Compute log prior probability
logjoint	DynamicPPL.logjoint	Compute log joint probability
condition	AbstractPPL.condition	Condition a model on data
decondition	AbstractPPL.decondition	Remove conditioning on data
conditioned	DynamicPPL.conditioned	Return the conditioned values of a model
fix	DynamicPPL.fix	Fix the value of a variable
unfix	DynamicPPL.unfix	Unfix the value of a variable
OrderedDict	OrderedCollections.OrderedDict	An ordered dictionary

Point estimates

See the mode estimation tutorial for more information.

Exported symbol	Documentation	Description
maximum_a_posteriori	Turing.Optimisation.maximum_a_posteriori	Find a MAP estimate for a model
maximum_likelihood	Turing.Optimisation.maximum_likelihood	Find a MLE estimate for a model
MAP	Turing.Optimisation.MAP	Type to use with Optim.jl for MAP estimation
MLE	Turing.Optimisation.MLE	Type to use with Optim.jl for MLE estimation