Use Random.randexp

src/trajectory.jl

@@ -133,7 +133,8 @@ $(TYPEDEF)
 Slice sampler for the starting single leaf tree.
 Slice variable is initialized.
 """
-SliceTS(rng::AbstractRNG, z0::PhasePoint) = SliceTS(z0, log(rand(rng)) - energy(z0), 1)
+SliceTS(rng::AbstractRNG, z0::PhasePoint) =
+    SliceTS(z0, neg_energy(z0) - Random.randexp(rng), 1)
 
 """
 $(TYPEDEF)
@@ -143,7 +144,7 @@ Multinomial sampler for the starting single leaf tree.
 
 Ref: https://github.com/stan-dev/stan/blob/develop/src/stan/mcmc/hmc/nuts/base_nuts.hpp#L226
 """
-MultinomialTS(rng::AbstractRNG, z0::PhasePoint) = MultinomialTS(z0, zero(energy(z0)))
+MultinomialTS(rng::AbstractRNG, z0::PhasePoint) = MultinomialTS(z0, zero(neg_energy(z0)))
 
 """
 $(TYPEDEF)
@@ -153,7 +154,7 @@ Create a slice sampler for a single leaf tree:
 - the number of acceptable candicates is computed by comparing the slice variable against the current energy.
 """
 function SliceTS(s::SliceTS, H0::AbstractFloat, zcand::PhasePoint)
-    return SliceTS(zcand, s.ℓu, (s.ℓu <= -energy(zcand)) ? 1 : 0)
+    return SliceTS(zcand, s.ℓu, Int(s.ℓu <= neg_energy(zcand)))
 end
 
 """
@@ -163,13 +164,13 @@ Multinomial sampler for a trajectory consisting only a leaf node.
 - tree weight is the (unnormalised) energy of the leaf.
 """
 function MultinomialTS(s::MultinomialTS, H0::AbstractFloat, zcand::PhasePoint)
-    return MultinomialTS(zcand, H0 - energy(zcand))
+    return MultinomialTS(zcand, H0 + neg_energy(zcand))
 end
 
 function combine(rng::AbstractRNG, s1::SliceTS, s2::SliceTS)
     @assert s1.ℓu == s2.ℓu "Cannot combine two slice sampler with different slice variable"
     n = s1.n + s2.n
-    zcand = rand(rng) < s1.n / n ? s1.zcand : s2.zcand
+    zcand = n * rand(rng) < s1.n ? s1.zcand : s2.zcand
     return SliceTS(zcand, s1.ℓu, n)
 end
 
@@ -181,7 +182,7 @@ end
 
 function combine(rng::AbstractRNG, s1::MultinomialTS, s2::MultinomialTS)
     ℓw = logaddexp(s1.ℓw, s2.ℓw)
-    zcand = rand(rng) < exp(s1.ℓw - ℓw) ? s1.zcand : s2.zcand
+    zcand = ℓw < s1.ℓw + Random.randexp(rng) ? s1.zcand : s2.zcand
     return MultinomialTS(zcand, ℓw)
 end
 
@@ -190,10 +191,10 @@ function combine(zcand::PhasePoint, s1::MultinomialTS, s2::MultinomialTS)
     return MultinomialTS(zcand, ℓw)
 end
 
-mh_accept(rng::AbstractRNG, s::SliceTS, s′::SliceTS) = rand(rng) < min(1, s′.n / s.n)
+mh_accept(rng::AbstractRNG, s::SliceTS, s′::SliceTS) = s.n * rand(rng) < s′.n
 
 function mh_accept(rng::AbstractRNG, s::MultinomialTS, s′::MultinomialTS)
-    return rand(rng) < min(1, exp(s′.ℓw - s.ℓw))
+    return s.ℓw < s′.ℓw + Random.randexp(rng)
 end
 
 """
@@ -696,16 +697,16 @@ function transition(
     j = 0
     while !isterminated(termination) && j < τ.termination_criterion.max_depth
         # Sample a direction; `-1` means left and `1` means right
-        v = rand(rng, [-1, 1])
-        if v == -1
+        vleft = rand(rng, Bool)
+        if vleft
             # Create a tree with depth `j` on the left
             tree′, sampler′, termination′ =
-                build_tree(rng, τ, h, tree.zleft, sampler, v, j, H0)
+                build_tree(rng, τ, h, tree.zleft, sampler, -1, j, H0)
             treeleft, treeright = tree′, tree
         else
             # Create a tree with depth `j` on the right
             tree′, sampler′, termination′ =
-                build_tree(rng, τ, h, tree.zright, sampler, v, j, H0)
+                build_tree(rng, τ, h, tree.zright, sampler, 1, j, H0)
             treeleft, treeright = tree, tree′
         end
         # Perform a MH step and increse depth if not terminated
@@ -842,8 +843,8 @@ function mh_accept_ratio(
     Horiginal::T,
     Hproposal::T,
 ) where {T<:AbstractFloat}
+    accept = Hproposal < Horiginal + Random.randexp(rng, T)
     α = min(one(T), exp(Horiginal - Hproposal))
-    accept = rand(rng, T) < α
     return accept, α
 end
 
@@ -852,12 +853,13 @@ function mh_accept_ratio(
     Horiginal::AbstractVector{<:T},
     Hproposal::AbstractVector{<:T},
 ) where {T<:AbstractFloat}
-    α = min.(one(T), exp.(Horiginal .- Hproposal))
     # NOTE: There is a chance that sharing the RNG over multiple
     #       chains for accepting / rejecting might couple
     #       the chains. We need to revisit this more rigirously 
     #       in the future. See discussions at 
     #       https://github.com/TuringLang/AdvancedHMC.jl/pull/166#pullrequestreview-367216534
-    accept = rand(rng, T, length(Horiginal)) .< α
+    _rng = rng isa AbstractRNG ? (rng,) : rng
+    accept = Hproposal .< Horiginal .+ Random.randexp.(_rng, (T,))
+    α = min.(one(T), exp.(Horiginal .- Hproposal))
     return accept, α
 end

test/abstractmcmc.jl

@@ -3,7 +3,7 @@ using Statistics: mean
 
 @testset "AbstractMCMC w/ gdemo" begin
     rng = MersenneTwister(0)
-    n_samples = 5_000
+    n_samples = 10_000
     n_adapts = 5_000
     θ_init = randn(rng, 2)