 # Multivariate dirichlet mixture with Turing

Hi – I’m trying to fit a mixture of 4 bivariate gaussians with Turing and getting a strange error? Can someone help with the syntax here:

@model GaussianMixtureModel(x, K) = begin
# K is expected number of clusters
# x is bivariate data, typically 2 x 1000

``````D, N = size(x)

# Draw the paramters
μ ~ [MvNormal([0.,0.], 5.) for i in 1:K]
τ ~ [Gamma() for i in 1:K]

# Dirichlet prior
α = 1.0
w ~ Dirichlet(K, α)

# Draw assignments for each datum and generate it from a multivariate normal.
k = Vector{Int}(undef, N)
for i in 1:N
k[i] ~ Categorical(w)
x[:, i] ~ MvNormal(μ[k[i]], τ[k[i]])
end

return k
``````

end

I’m getting error (when sampling): MethodError: no method matching assume(::DynamicPPL.SampleFromPrior,

Thanks

This should work.

``````@model GaussianMixtureModel(x, K) = begin
# K is expected number of clusters
# x is bivariate data, typically 2 x 1000

D, N = size(x)

# Draw the paramters
μ ~ filldist(MvNormal([0.,0.], 5.), K)
τ ~ filldist(Gamma(), K)

# Dirichlet prior
α = 1.0
w ~ Dirichlet(K, α)

# Draw assignments for each datum and generate it from a multivariate normal.
k = Vector{Int}(undef, N)
for i in 1:N
k[i] ~ Categorical(w)
x[:, i] ~ MvNormal(μ[:, k[i]], τ[k[i]])
end

return k
end
``````

Awesome - this works! Thanks

1 Like

Are there any issues sampling from MvNormal() as used in the model above?

gmm_model = GaussianMixtureModel(x, 8);
num_iter = 1000
samp_mh = sample(gmm_model, MH(), num_iter);
samp_hmc = sample(gmm_model, HMC(0.1, 5), num_iter);
samp_nuts = sample(gmm_model, NUTS(100, 0.65), num_iter);

If I try these three samplers MH() runs ok and completes. However both NUTS() and HMC() complain with the same error message: "TypeError: in typeassert, expected Int64, got ForwardDiff.Dual{Nothing,Int64,10}"

Any ideas? The synthetic dataset I’m trying to run this on is generated as follows:

y1 = rand(MvNormal([0., 0.], [[1, 0.] [0., 0.02]]), 250);
y2 = rand(MvNormal([0., 0.], [[0.02, 0.] [0., 1]]), 400);
y3 = rand(MvNormal([2., 2.], [[1., -0.9] [-0.9, 1]]), 500);
y4 = rand(MvNormal([-2., -2.], [[0.1, 0.] [0., 0.1]]), 300);
x = reduce(hcat, [y1, y2, y3, y4])

Thanks

You can’t use pure HMC when you have integer random variables, `k` above. You need `Gibbs` for that.

Does filldist() work as well for InverseWishart()? I’m having some issues setting up covariance prior as:

`τ ~ filldist(InverseWishart(2+1, Matrix{Float64}([[3,0.5] [0.5,3]])), K)`

and getting error message: “MethodError: no method matching filldist(::InverseWishart{Float64,PDMats.PDMat{Float64,Array{Float64,2}}}, ::Int64)”

Here’s the full model:

``````@model GaussianMixtureModel(x, K) = begin
# K is expected number of clusters
# x is bivariate data, typically 2 x 1000

D, N = size(x)

# Draw the paramters
μ ~ filldist(MvNormal([0.,0.], 5.), K)
τ ~ filldist(InverseWishart(2+1, Matrix{Float64}([[3,0.5] [0.5,3]])), K)

# Dirichlet prior
α = 1.0
w ~ Dirichlet(K, α)

# Draw assignments for each datum and generate it from a multivariate normal.
k = Vector{Int}(undef, N)
for i in 1:N
k[i] ~ Categorical(w)
x[:, i] ~ MvNormal(μ[:, k[i]], τ[:, :, k[i]])
end

return k
end
``````

Thanks