Bump Turing.jl to 0.36 and update all mentions of Gibbs to match

Author: mhauru

Project.toml

@@ -55,4 +55,4 @@ UnPack = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
-Turing = "0.35"
+Turing = "0.36"

_quarto.yml

@@ -32,7 +32,7 @@ website:
         text: Team
     right:
       # Current version
-      - text: "v0.35"
+      - text: "v0.36"
         menu:
           - text: Changelog
             href: https://turinglang.org/docs/changelog.html

developers/compiler/design-overview/index.qmd

@@ -291,8 +291,7 @@ not. Let `md` be an instance of `Metadata`:
     `md.vns`, `md.ranges`, `md.dists`, `md.orders` and `md.flags`.
   - `md.vns[md.idcs[vn]] == vn`.
   - `md.dists[md.idcs[vn]]` is the distribution of `vn`.
-  - `md.gids[md.idcs[vn]]` is the set of algorithms used to sample `vn`. This is used in
-    the Gibbs sampling process.
+  - `md.gids[md.idcs[vn]]` is the set of algorithms used to sample `vn`. This was used by the Gibbs sampler. Since Turing v0.36 it is unused and will eventually be deleted.
   - `md.orders[md.idcs[vn]]` is the number of `observe` statements before `vn` is sampled.
   - `md.ranges[md.idcs[vn]]` is the index range of `vn` in `md.vals`.
   - `md.vals[md.ranges[md.idcs[vn]]]` is the linearized vector of values of corresponding to `vn`.

tutorials/01-gaussian-mixture-model/index.qmd

@@ -110,7 +110,7 @@ model = gaussian_mixture_model(x);
 ```
 
 We run a MCMC simulation to obtain an approximation of the posterior distribution of the parameters $\mu$ and $w$ and assignments $k$.
-We use a `Gibbs` sampler that combines a [particle Gibbs](https://www.stats.ox.ac.uk/%7Edoucet/andrieu_doucet_holenstein_PMCMC.pdf) sampler for the discrete parameters (assignments $k$) and a Hamiltonion Monte Carlo sampler for the continuous parameters ($\mu$ and $w$).
+We use a `Gibbs` sampler that combines a [particle Gibbs](https://www.stats.ox.ac.uk/%7Edoucet/andrieu_doucet_holenstein_PMCMC.pdf) sampler for the discrete parameters (assignments $k$) and a Hamiltonian Monte Carlo sampler for the continuous parameters ($\mu$ and $w$).
 We generate multiple chains in parallel using multi-threading.
 
 ```{julia}
@@ -121,7 +121,7 @@ setprogress!(false)
 
 ```{julia}
 #| output: false
-sampler = Gibbs(PG(100, :k), HMC(0.05, 10, :μ, :w))
+sampler = Gibbs(:k => PG(100), (:μ, :w) => HMC(0.05, 10))
 nsamples = 150
 nchains = 4
 burn = 10

tutorials/04-hidden-markov-model/index.qmd

@@ -135,7 +135,7 @@ setprogress!(false)
 ```
 
 ```{julia}
-g = Gibbs(HMC(0.01, 50, :m, :T), PG(120, :s))
+g = Gibbs((:m, :T) => HMC(0.01, 50), :s => PG(120))
 chn = sample(BayesHmm(y, 3), g, 1000);
 ```
 

tutorials/docs-10-using-turing-autodiff/index.qmd

@@ -55,8 +55,8 @@ end
 c = sample(
     gdemo(1.5, 2),
     Gibbs(
-        HMC(0.1, 5, :m; adtype=AutoForwardDiff(; chunksize=0)),
-        HMC(0.1, 5, :s²; adtype=AutoReverseDiff(false)),
+        :m => HMC(0.1, 5; adtype=AutoForwardDiff(; chunksize=0)),
+        :s² => HMC(0.1, 5; adtype=AutoReverseDiff(false)),
     ),
     1000,
     progress=false,

tutorials/docs-12-using-turing-guide/index.qmd

@@ -61,7 +61,7 @@ We can perform inference by using the `sample` function, the first argument of w
 c1 = sample(gdemo(1.5, 2), SMC(), 1000)
 c2 = sample(gdemo(1.5, 2), PG(10), 1000)
 c3 = sample(gdemo(1.5, 2), HMC(0.1, 5), 1000)
-c4 = sample(gdemo(1.5, 2), Gibbs(PG(10, :m), HMC(0.1, 5, :s²)), 1000)
+c4 = sample(gdemo(1.5, 2), Gibbs(:m => PG(10), :s² => HMC(0.1, 5)), 1000)
 c5 = sample(gdemo(1.5, 2), HMCDA(0.15, 0.65), 1000)
 c6 = sample(gdemo(1.5, 2), NUTS(0.65), 1000)
 ```
@@ -450,7 +450,7 @@ end
 
 simple_choice_f = simple_choice([1.5, 2.0, 0.3])
 
-chn = sample(simple_choice_f, Gibbs(HMC(0.2, 3, :p), PG(20, :z)), 1000)
+chn = sample(simple_choice_f, Gibbs(:p => HMC(0.2, 3), :z => PG(20)), 1000)
 ```
 
 The `Gibbs` sampler can be used to specify unique automatic differentiation backends for different variable spaces. Please see the [Automatic Differentiation]({{<meta using-turing-autodiff>}}) article for more.

tutorials/docs-15-using-turing-sampler-viz/index.qmd

@@ -115,7 +115,7 @@ setprogress!(false)
 Gibbs sampling tends to exhibit a "jittery" trajectory. The example below combines `HMC` and `PG` sampling to traverse the posterior.
 
 ```{julia}
-c = sample(model, Gibbs(HMC(0.01, 5, :s²), PG(20, :m)), 1000)
+c = sample(model, Gibbs(:s² => HMC(0.01, 5), :m => PG(20)), 1000)
 plot_sampler(c)
 ```