diff --git a/src/combinators/mixture.jl b/src/combinators/mixture.jl
new file mode 100644
index 00000000..1f8bdd30
--- /dev/null
+++ b/src/combinators/mixture.jl
@@ -0,0 +1,105 @@
+# export Mixture
+
+using LinearAlgebra
+@parameterized Mixture(components, logweights)
+
+
+function logdens2(d::Mixture{(:components, :logweights)}, x::T) where {T}
+
+    components = d.components
+    logweights = d.logweights
+
+    # Ref: [https://www.nowozin.net/sebastian/blog/streaming-log-sum-exp-computation.html](https://www.nowozin.net/sebastian/blog/streaming-log-sum-exp-computation.html)
+
+    r, α = zero(T), T(-Inf)
+    s, β = zero(T), T(-Inf)
+
+    @inbounds @fastmath for i in eachindex(components)
+        dᵢ = components[i]
+        logwᵢ = logweights[i]
+        ℓᵢ = logdensity(dᵢ, x) + logwᵢ
+        
+        if ℓᵢ ≤ α
+            r += exp(ℓᵢ - α)
+        else
+            r *= exp(α - ℓᵢ)
+            r += one(T)
+            α = ℓᵢ
+        end
+
+        if logwᵢ ≤ β
+            s += exp(logwᵢ - β)
+        else
+            s *= exp(β - logwᵢ)
+            s += one(T)
+            β = logwᵢ
+        end
+    end
+
+    # ℓ = log(r) + α
+    # logtotal = log(s) + β
+
+    return log(r/s) + α - β
+end
+
+
+
+# function logdens1(d::Mixture{(:components, :logweights)}, x::T) where {T}
+
+#     components = d.components
+#     logweights = d.logweights
+
+#     # Ref: [https://www.nowozin.net/sebastian/blog/streaming-log-sum-exp-computation.html](https://www.nowozin.net/sebastian/blog/streaming-log-sum-exp-computation.html)
+
+#     r, α = zero(T), T(-Inf)
+#     s, β = zero(T), T(-Inf)
+
+#     @inbounds for i in eachindex(components, logweights)
+#         dᵢ = components[i]
+#         logwᵢ = logweights[i]
+#         ℓᵢ = logdensity(dᵢ, x) + logwᵢ
+        
+#         if ℓᵢ ≤ α
+#             r += exp(ℓᵢ - α)
+#         else
+#             r *= exp(α - ℓᵢ)
+#             r += one(T)
+#             α = ℓᵢ
+#         end
+
+#         if logwᵢ ≤ β
+#             s += exp(logwᵢ - β)
+#         else
+#             s *= exp(β - logwᵢ)
+#             s += one(T)
+#             β = logwᵢ
+#         end
+#     end
+
+#     # ℓ = log(r) + α
+#     # logtotal = log(s) + β
+
+#     return log(r/s) + α - β
+# end
+
+using StatsFuns: logsumexp
+
+function logdensity(d::Mixture{(:components, :logweights)}, x::T) where {T}
+    lp = @inbounds logsumexp((lw + logdensity(d.components[i], x) for (i,lw) in enumerate(d.logweights)))
+    return lp - logsumexp(d.logweights)
+end
+
+logp = [1.0, -1.0]
+p = exp.(logp)
+
+m = Mixture((Normal(), Cauchy()), tuple(logp...));
+d = Dists.MixtureModel([Dists.Normal(), Dists.Cauchy()], normalize(p,1))
+
+
+logdens2(m, 0.2)
+logdens3(m, 0.2)
+
+using BenchmarkTools
+
+@btime logdens2($m, 0.2)
+@btime logdens3($m, 0.2)