Replace old Gibbs sampler with the experimental one.

.JuliaFormatter.toml

@@ -1,11 +1,3 @@
 style="blue"
 format_markdown = true
 import_to_using = false
-# TODO
-# We ignore these files because when formatting was first put in place they were being worked on.
-# These ignores should be removed once the relevant PRs are merged/closed.
-ignore = [
-    # https://github.com/TuringLang/Turing.jl/pull/2328/files
-    "src/experimental/gibbs.jl",
-    "test/experimental/gibbs.jl",
-]

.github/workflows/Tests.yml

@@ -29,12 +29,10 @@ jobs:
             args: "mcmc/abstractmcmc.jl"
           - name: "mcmc/Inference"
             args: "mcmc/Inference.jl"
-          - name: "experimental/gibbs"
-            args: "experimental/gibbs.jl"
           - name: "mcmc/ess"
             args: "mcmc/ess.jl"
           - name: "everything else"
-            args: "--skip essential/ad.jl mcmc/gibbs.jl mcmc/hmc.jl mcmc/abstractmcmc.jl mcmc/Inference.jl experimental/gibbs.jl mcmc/ess.jl"
+            args: "--skip essential/ad.jl mcmc/gibbs.jl mcmc/hmc.jl mcmc/abstractmcmc.jl mcmc/Inference.jl mcmc/ess.jl"
         runner:
           # Default
           - version: '1'

HISTORY.md

@@ -1,3 +1,19 @@
+# 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

Project.toml

@@ -1,6 +1,6 @@
 name = "Turing"
 uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-version = "0.35.2"
+version = "0.36.0"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
@@ -49,7 +49,7 @@ TuringOptimExt = "Optim"
 
 [compat]
 ADTypes = "1.9"
-AbstractMCMC = "5.2"
+AbstractMCMC = "5.5"
 Accessors = "0.1"
 AdvancedHMC = "0.3.0, 0.4.0, 0.5.2, 0.6"
 AdvancedMH = "0.8"
@@ -63,7 +63,7 @@ Distributions = "0.23.3, 0.24, 0.25"
 DistributionsAD = "0.6"
 DocStringExtensions = "0.8, 0.9"
 DynamicHMC = "3.4"
-DynamicPPL = "0.29, 0.30.4"
+DynamicPPL = "0.30.4"
 EllipticalSliceSampling = "0.5, 1, 2"
 ForwardDiff = "0.10.3"
 Libtask = "0.8.8"

src/Turing.jl

@@ -55,7 +55,6 @@ using .Variational
 include("optimisation/Optimisation.jl")
 using .Optimisation
 
-include("experimental/Experimental.jl")
 include("deprecated.jl") # to be removed in the next minor version release
 
 ###########
@@ -86,7 +85,6 @@ export @model,                 # modelling
     Emcee,
     ESS,
     Gibbs,
-    GibbsConditional,
     HMC,                    # Hamiltonian-like sampling
     SGLD,
     SGHMC,
@@ -97,6 +95,7 @@ export @model,                 # modelling
     SMC,
     CSMC,
     PG,
+    RepeatSampler,
     vi,                     # variational inference
     ADVI,
     sample,                 # inference