Convolution between Gaussian mixture model and normal distribution

[Alexander-Barth]

It would be nice to allow the convolution between Gaussian mixture model and normal distribution, for example:

using Distributions # v0.25.113

d1 = MixtureModel(Normal, [(-2.0, 1.0), (2.0, 1.0)],[0.5,0.5])
d2 = Normal(0,1)

convolve(d1,d2)

Currently, this fails with:

ERROR: MethodError: no method matching convolve(::MixtureModel{Univariate, Continuous, Normal, Categorical{Float64, Vector{Float64}}}, ::Normal{Float64})

Support for this would be quite easy to add:

import Distributions: convolve, MixtureModel
# special case: https://mathoverflow.net/questions/370982/convolution-of-two-gaussian-mixture-model
Distributions.convolve(d1::MixtureModel,d2::Normal) =
    MixtureModel([convolve(c,d2) for c in d1.components],d1.prior)
Distributions.convolve(d1::Normal,d2::MixtureModel) = convolve(d2,d1)

d3 = convolve(d1,d2)

using Test

@test var(d3.components[1]) ≈ 2
@test var(d3.components[2]) ≈ 2
@test mean(d3.components[1]) ≈ -2
@test mean(d3.components[2]) ≈ 2
@test convolve(d1,d2) == convolve(d2,d1)

I can easily made a PR with the test case.