MH Constructor

src/Turing.jl

@@ -85,6 +85,7 @@ export  @model,                 # modelling
 
         MH,                     # classic sampling
         RWMH,
+        StaticMH,
         Emcee,
         ESS,
         Gibbs,

src/inference/Inference.jl

@@ -30,6 +30,7 @@ import AdvancedMH; const AMH = AdvancedMH
 import AdvancedPS
 import BangBang
 import ..Essential: getADbackend
+import DynamicPPL: extract_priors
 import EllipticalSliceSampling
 import LogDensityProblems
 import LogDensityProblemsAD
@@ -45,6 +46,8 @@ export  InferenceAlgorithm,
         SampleFromUniform,
         SampleFromPrior,
         MH,
+        RWMH,
+        StaticMH,
         ESS,
         Emcee,
         Gibbs,      # classic sampling

src/inference/mh.jl

@@ -188,6 +188,22 @@ function MH(space...)
     return MH{tuple(syms...), typeof(proposals)}(proposals)
 end
 
+function StaticMH(model::Model)
+    return MH(model; proposal_type=AMH.StaticProposal)
+end
+
+function RWMH(model::Model)
+    return MH(model; proposal_type=AMH.RandomWalkProposal)
+end
+
+function MH(model::Model; proposal_type=AMH.StaticProposal)
+    priors = extract_priors(model)
+    props = Tuple([proposal_type(prop) for prop in values(priors)])
+    vars = Symbol.(keys(priors))
+    priors = NamedTuple{Tuple(vars)}(props)
+    return AMH.MetropolisHastings(priors)
+end
+
 #####################
 # Utility functions #
 #####################

test/inference/mh.jl

@@ -17,6 +17,12 @@
 
         s4 = Gibbs(MH(:m), MH(:s))
         c4 = sample(gdemo_default, s4, N)
+
+        s5 = externalsampler(RWMH(gdemo_default))
+        c5 = sample(gdemo_default, s5, N)
+
+        s6 = externalsampler(StaticMH(gdemo_default))
+        c6 = sample(gdemo_default, s6, N)
     end
     @numerical_testset "mh inference" begin
         Random.seed!(125)