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Release 0.12.0

  • The interface for defining new distributions with a 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 yet.
  • There were some performance improvements in the automatic differentiation 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 favor 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 processes (you can add processes with addprocs()).
  • Turing now uses AdvancedMH v0.5, which mostly provides behind-the -scenes restructuring.
  • Custom expressions and macros can be interpolated in the @model definition with $ and it is possible to use @. also for assumptions 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