diff --git a/docs/src/convolution.md b/docs/src/convolution.md
index 352235675..07bf7a281 100644
--- a/docs/src/convolution.md
+++ b/docs/src/convolution.md
@@ -4,7 +4,8 @@ A [convolution of two probability distributions](https://en.wikipedia.org/wiki/L
 is the probability distribution of the sum of two independent random variables that are
 distributed according to these distributions.
 
-The convolution of two distributions can be constructed with [`convolve`](@ref).
+The convolution of two distributions can be constructed with [`convolve`](@ref) or its
+Unicode alias [`⊕`](@ref).
 
 ```@docs
 convolve
diff --git a/src/Distributions.jl b/src/Distributions.jl
index 3cf0a2812..375afc4f2 100644
--- a/src/Distributions.jl
+++ b/src/Distributions.jl
@@ -190,6 +190,7 @@ export
     componentwise_logpdf,   # component-wise logpdf for mixture models
     concentration,      # the concentration parameter
     convolve,           # convolve distributions of the same type
+    ⊕,                  # unicode alias of convolve
     dim,                # sample dimension of multivariate distribution
     dof,                # get the degree of freedom
     entropy,            # entropy of distribution in nats
diff --git a/src/convolution.jl b/src/convolution.jl
index 6c18b70e4..fc52a1c37 100644
--- a/src/convolution.jl
+++ b/src/convolution.jl
@@ -1,9 +1,12 @@
 """
     convolve(d1::Distribution, d2::Distribution)
+    d1 ⊕ d2
 
 Convolve two distributions and return the distribution corresponding to the sum of
 independent random variables drawn from the underlying distributions.
 
+The Unicode operator `⊕` can be typed by `\\oplus<tab>`.
+
 Currently, the function is only defined in cases where the convolution has a closed form.
 More precisely, the function is defined if the distributions of `d1` and `d2` are the same
 and one of
@@ -68,6 +71,9 @@ function convolve(d1::MvNormal, d2::MvNormal)
     return MvNormal(d1.μ + d2.μ, d1.Σ + d2.Σ)
 end
 
+# define Unicode alias
+const ⊕ = convolve
+
 function _check_convolution_args(p1, p2)
     p1 ≈ p2 || throw(ArgumentError(
     "$(p1) !≈ $(p2): distribution parameters must be approximately equal",
diff --git a/test/convolution.jl b/test/convolution.jl
index 826e4f82c..97800f5b0 100644
--- a/test/convolution.jl
+++ b/test/convolution.jl
@@ -7,64 +7,75 @@ using Test
 @testset "discrete univariate" begin
     @testset "Bernoulli" begin
         d1 = Bernoulli(0.1)
-        d2 = @inferred(convolve(d1, d1))
-        @test d2 isa Binomial
-        @test d2.n == 2
-        @test d2.p == 0.1
-
-        # cannot convolve a Binomial with a Bernoulli
-        @test_throws MethodError convolve(d1, d2)
+        for d2 in (@inferred(convolve(d1, d1)), @inferred(d1 ⊕ d1))
+            @test d2 isa Binomial
+            @test d2.n == 2
+            @test d2.p == 0.1
+
+            # cannot convolve a Binomial with a Bernoulli
+            @test_throws MethodError convolve(d1, d2)
+            @test_throws MethodError d1 ⊕ d2
+        end
 
         # only works if p1 ≈ p2
         d3 = Bernoulli(0.2)
         @test_throws ArgumentError convolve(d1, d3)
+        @test_throws ArgumentError d1 ⊕ d3
     end
 
     @testset "Binomial" begin
         d1 = Binomial(2, 0.1)
         d2 = Binomial(5, 0.1)
-        d3 = @inferred(convolve(d1, d2))
-        @test d3 isa Binomial
-        @test d3.n == 7
-        @test d3.p == 0.1
+        for d3 in (@inferred(convolve(d1, d2)), @inferred(d1 ⊕ d2))
+            @test d3 isa Binomial
+            @test d3.n == 7
+            @test d3.p == 0.1
+        end
 
         # only works if p1 ≈ p2
         d4 = Binomial(2, 0.2)
         @test_throws ArgumentError convolve(d1, d4)
+        @test_throws ArgumentError d1 ⊕ d4
     end
 
     @testset "NegativeBinomial" begin
         d1 = NegativeBinomial(4, 0.1)
         d2 = NegativeBinomial(1, 0.1)
-        d3 = @inferred(convolve(d1, d2))
-        @test d3 isa NegativeBinomial
-        @test d3.r == 5
-        @test d3.p == 0.1
+        for d3 in (@inferred(convolve(d1, d2)), @inferred(d1 ⊕ d2))
+            @test d3 isa NegativeBinomial
+            @test d3.r == 5
+            @test d3.p == 0.1
+        end
 
         d4 = NegativeBinomial(1, 0.2)
         @test_throws ArgumentError convolve(d1, d4)
+        @test_throws ArgumentError d1 ⊕ d4
     end
 
     @testset "Geometric" begin
         d1 = Geometric(0.2)
-        d2 = convolve(d1, d1)
-        @test d2 isa NegativeBinomial
-        @test d2.p == 0.2
+        for d2 in (@inferred(convolve(d1, d1)), @inferred(d1 ⊕ d1))
+            @test d2 isa NegativeBinomial
+            @test d2.p == 0.2
 
-        # cannot convolve a Geometric with a NegativeBinomial
-        @test_throws MethodError convolve(d1, d2)
+            # cannot convolve a Geometric with a NegativeBinomial
+            @test_throws MethodError convolve(d1, d2)
+            @test_throws MethodError d1 ⊕ d2
+        end
 
         # only works if p1 ≈ p2
         d3 = Geometric(0.5)
         @test_throws ArgumentError convolve(d1, d3)
+        @test_throws ArgumentError d1 ⊕ d3
     end
 
     @testset "Poisson" begin
         d1 = Poisson(0.1)
         d2 = Poisson(0.4)
-        d3 = @inferred(convolve(d1, d2))
-        @test d3 isa Poisson
-        @test d3.λ == 0.5
+        for d3 in (@inferred(convolve(d1, d2)), @inferred(d1 ⊕ d2))
+            @test d3 isa Poisson
+            @test d3.λ == 0.5
+        end
     end
 end
 
@@ -72,51 +83,58 @@ end
     @testset "Gaussian" begin
         d1 = Normal(0.1, 0.2)
         d2 = Normal(0.25, 1.7)
-        d3 = @inferred(convolve(d1, d2))
-        @test d3 isa Normal
-        @test d3.μ == 0.35
-        @test d3.σ == hypot(0.2, 1.7)
+        for d3 in (@inferred(convolve(d1, d2)), @inferred(d1 ⊕ d2))
+            @test d3 isa Normal
+            @test d3.μ == 0.35
+            @test d3.σ == hypot(0.2, 1.7)
+        end
     end
 
     @testset "Cauchy" begin
         d1 = Cauchy(0.2, 0.7)
         d2 = Cauchy(1.9, 0.8)
-        d3 = @inferred(convolve(d1, d2))
-        @test d3 isa Cauchy
-        @test d3.μ == 2.1
-        @test d3.σ == 1.5
+        for d3 in (@inferred(convolve(d1, d2)), @inferred(d1 ⊕ d2))
+            @test d3 isa Cauchy
+            @test d3.μ == 2.1
+            @test d3.σ == 1.5
+        end
     end
 
     @testset "Chisq" begin
         d1 = Chisq(0.1)
         d2 = Chisq(0.3)
-        d3 = @inferred(convolve(d1, d2))
-        @test d3 isa Chisq
-        @test d3.ν == 0.4
+        for d3 in (@inferred(convolve(d1, d2)), @inferred(d1 ⊕ d2))
+            @test d3 isa Chisq
+            @test d3.ν == 0.4
+        end
     end
 
     @testset "Exponential" begin
         d1 = Exponential(0.7)
-        d2 = @inferred(convolve(d1, d1))
-        @test d2 isa Gamma
-        @test d2.α == 2
-        @test d2.θ == 0.7
-
-        # cannot convolve an Exponential with a Gamma
-        @test_throws MethodError convolve(d1, d2)
+        for d2 in (@inferred(convolve(d1, d1)), @inferred(d1 ⊕ d1))
+            @test d2 isa Gamma
+            @test d2.α == 2
+            @test d2.θ == 0.7
+
+            # cannot convolve an Exponential with a Gamma
+            @test_throws MethodError convolve(d1, d2)
+            @test_throws MethodError d1 ⊕ d2
+        end
 
         # only works if θ1 ≈ θ2
         d3 = Exponential(0.2)
         @test_throws ArgumentError convolve(d1, d3)
+        @test_throws ArgumentError d1 ⊕ d3
     end
 
     @testset "Gamma" begin
         d1 = Gamma(0.1, 1.7)
         d2 = Gamma(0.5, 1.7)
-        d3 = @inferred(convolve(d1, d2))
-        @test d3 isa Gamma
-        @test d3.α == 0.6
-        @test d3.θ == 1.7
+        for d3 in (@inferred(convolve(d1, d2)), @inferred(d1 ⊕ d2))
+            @test d3 isa Gamma
+            @test d3.α == 0.6
+            @test d3.θ == 1.7
+        end
 
         # only works if θ1 ≈ θ4
         d4 = Gamma(1.2, 0.4)
@@ -153,18 +171,21 @@ end
         dist_list = (in1, in2, zmin1, zmin2, dn1, dn2, zmdn1, zmdn2, fn1, fn2, zm1, zm2)
 
         for (d1, d2) in Iterators.product(dist_list, dist_list)
-            d3  = @inferred(convolve(d1, d2))
-            @test d3 isa MvNormal
-            @test d3.μ == d1.μ .+ d2.μ
-            @test Matrix(d3.Σ) == Matrix(d1.Σ + d2.Σ)  # isequal not defined for PDMats
+            for d3 in (@inferred(convolve(d1, d2)), @inferred(d1 ⊕ d2))
+                @test d3 isa MvNormal
+                @test d3.μ == d1.μ .+ d2.μ
+                @test Matrix(d3.Σ) == Matrix(d1.Σ + d2.Σ)  # isequal not defined for PDMats
+            end
         end
 
         # erroring
         in3 = MvNormal([1, 2, 3], 0.2 * I)
         @test_throws ArgumentError convolve(in1, in3)
+        @test_throws ArgumentError in1 ⊕ in3
 
         m5 = Symmetric(rand(3, 3))
         fn3 = MvNormal(zeros(3), m5^2)
         @test_throws ArgumentError convolve(fn1, fn3)
+        @test_throws ArgumentError fn1 ⊕ fn3
     end
 end