diff --git a/src/Turing.jl b/src/Turing.jl
index 11dcbdb6f..33286a665 100644
--- a/src/Turing.jl
+++ b/src/Turing.jl
@@ -73,7 +73,6 @@ export  @model,                 # modelling
         Prior,                  # Sampling from the prior
 
         MH,                     # classic sampling
-        RWMH,
         Emcee,
         ESS,
         Gibbs,
diff --git a/src/inference/Inference.jl b/src/inference/Inference.jl
index 73f190dcc..45ae434a4 100644
--- a/src/inference/Inference.jl
+++ b/src/inference/Inference.jl
@@ -99,6 +99,24 @@ Wrap a sampler so it can be used as an inference algorithm.
 """
 externalsampler(sampler::AbstractSampler) = ExternalSampler(sampler)
 
+"""
+    ESLogDensityFunction
+
+A log density function for the External sampler.
+
+"""
+const ESLogDensityFunction{M<:Model,S<:Sampler{<:ExternalSampler},V<:AbstractVarInfo} = Turing.LogDensityFunction{V,M,<:DynamicPPL.DefaultContext}
+function LogDensityProblems.logdensity(f::ESLogDensityFunction, x::NamedTuple)
+    return DynamicPPL.logjoint(f.model, DynamicPPL.unflatten(f.varinfo, x))
+end
+
+# TODO: make a nicer `set_namedtuple!` and move these functions to DynamicPPL.
+function DynamicPPL.unflatten(vi::TypedVarInfo, θ::NamedTuple) 
+    set_namedtuple!(deepcopy(vi), θ)
+    return vi
+end
+DynamicPPL.unflatten(vi::SimpleVarInfo, θ::NamedTuple) = SimpleVarInfo(θ, vi.logp, vi.transformation)
+
 # Algorithm for sampling from the prior
 struct Prior <: InferenceAlgorithm end
 
diff --git a/src/inference/mh.jl b/src/inference/mh.jl
index ddbeaa2c5..dd97efd18 100644
--- a/src/inference/mh.jl
+++ b/src/inference/mh.jl
@@ -188,6 +188,20 @@ function MH(space...)
     return MH{tuple(syms...), typeof(proposals)}(proposals)
 end
 
+# Some of the proposals require working in unconstrained space.
+transform_maybe(proposal::AMH.Proposal) = proposal
+function transform_maybe(proposal::AMH.RandomWalkProposal)
+    return AMH.RandomWalkProposal(Bijectors.transformed(proposal.proposal))
+end
+
+function MH(model::Model; proposal_type=AMH.StaticProposal)
+    priors = DynamicPPL.extract_priors(model)
+    props = Tuple([proposal_type(prop) for prop in values(priors)])
+    vars = Tuple(map(Symbol, collect(keys(priors))))
+    priors = map(transform_maybe, NamedTuple{vars}(props))
+    return AMH.MetropolisHastings(priors)
+end
+
 #####################
 # Utility functions #
 #####################
@@ -346,6 +360,7 @@ end
 function should_link(varinfo, sampler, proposal::AdvancedMH.RandomWalkProposal)
     return true
 end
+# FIXME: This won't be hit unless `vals` are all the exactly same concrete type of `AdvancedMH.RandomWalkProposal`!
 function should_link(
     varinfo,
     sampler,
diff --git a/test/inference/mh.jl b/test/inference/mh.jl
index 8e52aec9b..94f9aa992 100644
--- a/test/inference/mh.jl
+++ b/test/inference/mh.jl
@@ -17,6 +17,12 @@
 
         s4 = Gibbs(MH(:m), MH(:s))
         c4 = sample(gdemo_default, s4, N)
+
+        s5 = externalsampler(MH(gdemo_default, proposal_type=AdvancedMH.RandomWalkProposal))
+        c5 = sample(gdemo_default, s5, N)
+
+        s6 = externalsampler(MH(gdemo_default, proposal_type=AdvancedMH.StaticProposal))
+        c6 = sample(gdemo_default, s6, N)
     end
     @numerical_testset "mh inference" begin
         Random.seed!(125)