redo of TuringLang#41

src/mh-core.jl

@@ -191,6 +191,41 @@ function AbstractMCMC.step(
     return transition, transition
 end
 
+"""
+    is_symmetric_proposal(proposal::P) where P
+
+# Example:
+
+```julia
+using Distributions, AdvancedMH
+
+# Model definition.
+model = DensityModel(s -> logpdf(Normal(), s.x) + logpdf(Normal(5,.7), s.y))
+
+# Set up the proposal.
+p = (x=RandomWalkProposal(Normal(0,.5)), y=RandomWalkProposal(Normal(0,.5)))
+
+# Implementing this will skip the computation of the Hastings ratio.
+AdvancedMH.is_symmetric_proposal(proposal::typeof(p)) = true
+
+# Sample from the posterior with initial parameters.
+chain = sample(m1, MetropolisHastings(p), 100000; chain_type=Vector{NamedTuple})
+```
+"""
+is_symmetric_proposal(proposal::P) where P = false
+
+# The following univariate random walk proposals are symmetric.
+is_symmetric_proposal(proposal::RandomWalkProposal{<:Normal}) = true
+is_symmetric_proposal(proposal::RandomWalkProposal{<:MvNormal}) = true
+is_symmetric_proposal(proposal::RandomWalkProposal{<:TDist}) = true
+is_symmetric_proposal(proposal::RandomWalkProposal{<:Cauchy}) = true
+
+# The following multivariate random walk proposals are symmetric.
+is_symmetric_proposal(proposal::RandomWalkProposal{AbstractArray{<:Normal}}) = true
+is_symmetric_proposal(proposal::RandomWalkProposal{AbstractArray{<:MvNormal}}) = true
+is_symmetric_proposal(proposal::RandomWalkProposal{AbstractArray{<:TDist}}) = true
+is_symmetric_proposal(proposal::RandomWalkProposal{AbstractArray{<:Cauchy}}) = true
+
 # Define the other sampling steps.
 # Return a 2-tuple consisting of the next sample and the the next state.
 # In this case they are identical, and either a new proposal (if accepted)
@@ -206,8 +241,10 @@ function AbstractMCMC.step(
     params = propose(rng, spl, model, params_prev)
 
     # Calculate the log acceptance probability.
-    logα = logdensity(model, params) - logdensity(model, params_prev) +
-        q(spl, params_prev, params) - q(spl, params, params_prev)
+    logα = logdensity(model, params) - logdensity(model, params_prev)
+    if is_symmetric_proposal(spl.proposal)
+      logα += q(spl, params_prev, params) - q(spl, params, params_prev)
+    end
 
     # Decide whether to return the previous params or the new one.
     if -Random.randexp(rng) < logα

test/runtests.jl

@@ -104,7 +104,24 @@ using ForwardDiff
 
         @test chain1[1].params == val
     end
-
+
+    @testset "is_symmetric_proposal" begin
+        # Model definition.
+        m1 = DensityModel(s -> logpdf(Normal(), s.x) + logpdf(Normal(5,.7), s.y))
+
+        # Set up the proposal.
+        p1 = (x=RandomWalkProposal(Normal(0,.5)), y=RandomWalkProposal(Normal(0,.5)))
+        AdvancedMH.is_symmetric_proposal(proposal::typeof(p1)) = true
+
+        # Sample from the posterior with initial parameters.
+        chain1 = sample(m1, MetropolisHastings(p1), 100000; chain_type=Vector{NamedTuple})
+
+        @test mean(getindex.(chain1, :x)) ≈ 0 atol=0.05
+        @test mean(getindex.(chain1, :y)) ≈ 5 atol=0.05
+        @test std(getindex.(chain1, :x)) ≈ 1 atol=0.05
+        @test std(getindex.(chain1, :y)) ≈ .7 atol=0.05
+    end
+
     @testset "MALA" begin
 
         # Set up the sampler.