Tutorial 'Bayesian Mixed Models': PG is ok, Gibbs runs into trouble

[CMoebus]

Hi,
As a kind of exercise I try to squeeze the code a little bit. Sampling only with PG(...) seems to give reasonable results. But when I try to use the Gibbs(...) sampler, as the original does it generates an error message rather mysterious to me. I provide the code in a Pluto cell below. It would help a lot if I could get a hint what is going wrong.
Thanks a lot, Claus

let N = 60
K = 2
D = 2
w = [ 0.5, 0.5] # w is fixed
μ0 = [-3.5 +0.5; # μ0 = [μ0[1], μ0[2]] for data generation
+3.5 -0.5]
μ1 = [ 0.0 0.0; # μ1 = [μ1[1], μ1[2]] as priors
0.0 0.0]
μ = Array{Float64}(undef, (D, K))
x = Array{Float64}(undef, (D, N))
# ===============================================================================
# Data Generation
#--------------------------------------------------------------------------------
mixtureModel0 = MixtureModel([MvNormal(μ0[:,k], I) for k in 1:K], w)
# ^==== explicit column vectors
x = rand(mixtureModel0, N)
# ^======================== continuous cluster membership
# ===============================================================================
# Mixture Model
#--------------------------------------------------------------------------------
@model function bayesianMixtureModel(x)
k = Vector{Int64}(undef, N)
#----------------------------------------------------------------------------
for k in 1:K
# sampling priors μ1 = [μ1[1], μ1[2]]
μ[:, k] ~ MvNormal(μ1[:,k], 2.0 .* I)
end # for k
#
mixtureModel = [MvNormal(μ[:,k], I) for k in 1:K] # <=== discrete membership
# ^==== explicit column vectors
for i in 1:N
k[i] ~ Categorical(w) # k[i], vector is important
# ^======================== discrete cluster membership
x[:,i] ~ mixtureModel[k[i]] # likelihood
end # for i
end # function bayesianMixtureModel
#--------------------------------------------------------------------------------
model = bayesianMixtureModel(x)
#--------------------------------------------------------------------------------
chains =
let nSamples = 100
# sampler = Prior()
# sampler = PG(100, :k, :μ) # <========= seems ok
sampler = Gibbs(PG(100, :k), HMC(0.05, 10, :μ))
sample(model, sampler, nSamples)
end # let
#--------------------------------------------------------------------------------
plot(chains[["μ[:,1][1]", "μ[:,1][2]", "μ[:,2][1]", "μ[:,2][2]"]], colordim = :parameter, legend=true)
#--------------------------------------------------------------------------------
assignments = [mean(chains, "k[$i]") for i in 1:N]
#--------------------------------------------------------------------------------
μMean1 = [mean(chains, "μ[:,1][$i]") for i in 1:D]
μMean2 = [mean(chains, "μ[:,2][$i]") for i in 1:D]
# ===============================================================================
scatter(x[1, :], x[2, :]; label="data point", title=L"Data 3 (Discrete Post. Membership $k[i]$ with Turing)", zcolor=assignments)
#--------------------------------------------------------------------
# plot of the prior cluster centroids
scatter!([(μ0[1, 1], μ0[2, 1]), (μ0[1, 2], μ0[2, 2])], color=:turquoise, label="prior cluster centroid", markersize=6)
#
# plot of the posterior cluster centroids
scatter!([(μMean1[1], μMean1[2]), (μMean2[1], μMean2[2])], color=:violet, label="posterior cluster centroid", markersize=6)
#--------------------------------------------------------------------------------
end # let