I am trying to implement bayesian multidimensional scaling in Turing (from this paper: https://sites.stat.washington.edu/raftery/Research/PDF/oh2001.pdf). I have a model that I think is correct, but it seems no samples get accepted, so the sampler never terminates (or updates the ETA).

Here is my code, I use the s-curve from ManifoldLearning as test data.

```
using Turing, StatsPlots, Random, LinearAlgebra, MultivariateStats, ManifoldLearning
using Plots
@model function bmds(p_max,d)
p ~ DiscreteUniform(1,p_max)
n = size(dm)[1]
xs = Vector{Vector{Real}}(undef,n)
#priors for variance
σ2 ~ Exponential()
#priors for points, just put them at zero
for i in 1:n
xs[i] ~ MvNormal(zeros(p),Diagonal(ones(p)))
end
for i in 1:n
for j in 1:(i-1)
δ_ij = sqrt(sum((xs[i] .- xs[j]).^2))
d[i,j] ~ truncated(Normal(δ_ij,σ2); lower = 0.0)
end
end
end
#test dataset
test_dataset = ManifoldLearning.scurve()[1]
dist_matrix(pts) = reshape([sum((pts[:,i] .- pts[:,j]).^2) for i in 1:size(pts)[2], j in 1:size(pts)[2]],(size(pts)[2],size(pts)[2]))
dm = dist_matrix(test_dataset)
chain = sample( bmds(3, dm), SMC(10_000), MCMCThreads(), 1_500, 10)
describe(chain)
```

in general, I am not sure how to peer into the @model macro.

edit: Hong Ge on Slack pointed out a mistake in the model specification, and that HMC does not support discrete variables. However, this model still does not yet work.