Changelog

Release 0.36.0

Breaking changes

0.36.0 introduces a new Gibbs sampler. It’s been included in several previous releases as Turing.Experimental.Gibbs, but now takes over the old Gibbs sampler, which gets removed completely.

The new Gibbs sampler supports the same user-facing interface as the old one. However, given that the internals of it having been completely rewritten in a very different manner, there may be accidental breakage that we haven’t anticipated. Please report any you find.

GibbsConditional has also been removed. It was never very user-facing, but it was exported, so technically this is breaking.

The old Gibbs constructor relied on being called with several subsamplers, and each of the constructors of the subsamplers would take as arguments the symbols for the variables that they are to sample, e.g. Gibbs(HMC(:x), MH(:y)). This constructor has been deprecated, and will be removed in the future. The new constructor works by assigning samplers to either symbols or VarNames, e.g. Gibbs(; x=HMC(), y=MH()) or Gibbs(@varname(x) => HMC(), @varname(y) => MH()). This allows more granular specification of which sampler to use for which variable.

Likewise, the old constructor for calling one subsampler more often than another, Gibbs((HMC(0.01, 4, :x), 2), (MH(:y), 1)) has been deprecated. The new way to do this is to use RepeatSampler, also introduced at this version: Gibbs(@varname(x) => RepeatSampler(HMC(0.01, 4), 2), @varname(y) => MH()).

Release 0.35.0

Breaking changes

Julia 1.10 is now the minimum required version for Turing.

Tapir.jl has been removed and replaced with its successor, Mooncake.jl. You can use Mooncake.jl by passing adbackend=AutoMooncake(; config=nothing) to the relevant samplers.

Support for Tracker.jl as an AD backend has been removed.

Release 0.33.0

Breaking changes

The following exported functions have been removed:

  • constrained_space
  • get_parameter_bounds
  • optim_objective
  • optim_function
  • optim_problem

The same functionality is now offered by the new exported functions

  • maximum_likelihood
  • maximum_a_posteriori

Release 0.30.5

  • essential/ad.jl is removed, ForwardDiff and ReverseDiff integrations via LogDensityProblemsAD are moved to DynamicPPL and live in corresponding package extensions.
  • LogDensityProblemsAD.ADgradient(ℓ::DynamicPPL.LogDensityFunction) (i.e. the single argument method) is moved to Inference module. It will create ADgradient using the adtype information stored in context field of ℓ.
  • getADbackend function is renamed to getADType, the interface is preserved, but packages that previously used getADbackend should be updated to use getADType.
  • TuringTag for ForwardDiff is also removed, now DynamicPPLTag is defined in DynamicPPL package and should serve the same purpose.

Release 0.30.0

  • ADTypes.jl replaced Turing’s global AD backend. Users should now specify the desired ADType directly in sampler constructors, e.g., HMC(0.1, 10; adtype=AutoForwardDiff(; chunksize)), or HMC(0.1, 10; adtype=AutoReverseDiff(false)) (false indicates not to use compiled tape).
  • Interface functions such as ADBackend, setadbackend, setadsafe, setchunksize, and setrdcache are deprecated and will be removed in a future release.
  • Removed the outdated verifygrad function.
  • Updated to a newer version of LogDensityProblemsAD (v1.7).

Release 0.12.0

  • The interface for defining new distributions with constrained support and making them compatible with Turing has changed. To make a custom distribution type CustomDistribution compatible with Turing, the user needs to define the method bijector(d::CustomDistribution) that returns an instance of type Bijector implementing the Bijectors.Bijector API.
  • ~ is now thread-safe when used for observations, but not assumptions (non-observed model parameters) yet.
  • There were some performance improvements in the automatic differentiation (AD) of functions in DistributionsAD and Bijectors, leading to speeds closer to and sometimes faster than Stan’s.
  • An HMC initialization bug was fixed. HMC initialization in Turing is now consistent with Stan’s.
  • Sampling from the prior is now possible using sample.
  • psample is now deprecated in favour of sample(model, sampler, parallel_method, n_samples, n_chains) where parallel_method can be either MCMCThreads() or MCMCDistributed(). MCMCThreads will use your available threads to sample each chain (ensure that you have the environment variable JULIA_NUM_THREADS set to the number of threads you want to use), and MCMCDistributed will dispatch chain sampling to each available process (you can add processes with addprocs()).
  • Turing now uses AdvancedMH.jl v0.5, which mostly provides behind-the-scenes restructuring.
  • Custom expressions and macros can be interpolated in the @model definition with $; it is possible to use @. also for assumptions (non-observed model parameters) and observations.
  • The macros @varinfo, @logpdf, and @sampler are removed. Instead, one can access the internal variables _varinfo, _model, _sampler, and _context in the @model definition.
  • Additional constructors for SMC and PG make it easier to choose the resampling method and threshold.

Release 0.11.0

  • Removed some extraneous imports and dependencies (#1182)
  • Minor backend changes to sample and psample, which now use functions defined upstream in AbstractMCMC.jl (#1187)
  • Fix for an AD-related crash (#1202)
  • StatsBase compat update to 0.33 (#1185)
  • Bugfix for ReverseDiff caching and memoization (#1208)
  • BREAKING: VecBinomialLogit is now removed. Also BernoulliLogit is added (#1214)
  • Bugfix for cases where dynamic models were breaking with HMC methods (#1217)
  • Updates to allow AdvancedHMC 0.2.23 (#1218)
  • Add more informative error messages for SMC (#900)

Release 0.10.1

  • Fix bug where arrays with mixed integers, floats, and missing values were not being passed to the MCMCChains.Chains constructor properly #1180.

Release 0.10.0

  • Update elliptical slice sampling to use EllipticalSliceSampling.jl on the backend. #1145. Nothing should change from a front-end perspective – you can still call sample(model, ESS(), 1000).
  • Added default progress loggers in #1149.
  • The symbols used to define the AD backend have changed to be the lowercase form of the package name used for AD. forward_diff is now forwarddiff, reverse_diff is now tracker, and zygote and reversediff are newly supported (see below). forward_diff and reverse_diff are deprecated and are slated to be removed.
  • Turing now has experimental support for Zygote.jl (#783) and ReverseDiff.jl (#1170) AD backends. Both backends are experimental, so please report any bugs you find. Zygote does not allow mutation within your model, so please be aware of this issue. You can enable Zygote with Turing.setadbackend(:zygote) and you can enable ReverseDiff with Turing.setadbackend(:reversediff), though to use either you must import the package with using Zygote or using ReverseDiff. for loops are not recommended for ReverseDiff or Zygote – see performance tips for more information.
  • Fix MH indexing bug #1135.
  • Fix MH array sampling #1167.
  • Fix bug in VI where the bijectors where being inverted incorrectly #1168.
  • The Gibbs sampler handles state better by passing Transition structs to the local samplers (#1169 and #1166).

Release 0.4.0-alpha

  • Fix compatibility with Julia 0.6 [#341, #330, #293]
  • Support of Stan interface [#343, #326]
  • Fix Binomial distribution for gradients. [#311]
  • Stochastic gradient Hamiltonian Monte Carlo [#201]; Stochastic gradient Langevin dynamics [#27]
  • More particle MCMC family samplers: PIMH & PMMH [#364, #369]
  • Disable adaptive resampling for CSMC [#357]
  • Fix resampler for SMC [#338]
  • Interactive particle MCMC [#334]
  • Add type alias CSMC for PG [#333]
  • Fix progress meter [#317]

Release 0.3

  • NUTS implementation #188
  • HMC: Transforms of ϵ for each variable #67 (replace with introducing mass matrix)
  • Finish: Sampler (internal) interface design #107
  • Substantially improve performance of HMC and Gibbs #7
  • Vectorising likelihood computations #117 #255
  • Remove obsolete randoc, randc? #156
  • Support truncated distribution. #87
  • Refactoring code: Unify VarInfo, Trace, TaskLocalStorage #96
  • Refactoring code: Better gradient interface #97

Release 0.2

  • Gibbs sampler ([#73])
  • HMC for constrained variables ([#66]; no support for varying dimensions)
  • Added support for Mamba.Chain ([#90]): describe, plot etc.
  • New interface design ([#55]), ([#104])
  • Bugfixes and general improvements (e.g. VarInfo [#96])

Release 0.1.0

  • Initial support for Hamiltonian Monte Carlo (no support for discrete/constrained variables)
  • Require Julia 0.5
  • Bugfixes and general improvements

Release 0.0.1-0.0.4

The initial releases of Turing.

  • Particle MCMC, SMC, IS
  • Implemented copying for Julia Task
  • Implemented copy-on-write data structure TArray for Tasks
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