Hello everyone,

I am fairly new to Stan and currently play around with a mixture model using a truncated DP with a fixed number of components K=5. Every observation \mathbf{x} \in \{ 0, 1\}^p is modelled as

where \boldsymbol \pi is a vector of mixing weights and \boldsymbol M a K \times p-dimensional matrix of success-probabilities for the Bernoullis. I am afraid the problem is fairly simple to detect but I cannot spot it. I guess, something with my model specification seems to be flawed, because from 10000 samples I get like 9900 divergent transitions or so. I ordered the matrix of success probabilities and use an `inv_logit`

transform to keep them in bounds, but that didnâ€™t help. Could anyone help please?

Below is the Stan code. The code in `transformed parameters`

is the stick-breaking construction for the mixing weights.

Thank you,

Simon

```
data {
int<lower=1> K;
int<lower=1> n;
int<lower=1> p;
real<lower=0> a;
real<lower=0> b;
int<lower=0,upper=1> x[n, p];
real<lower=1> alpha;
}
parameters {
ordered[p] rates[K];
real<lower=0, upper=1> nu[K];
}
transformed parameters {
simplex[K] pi;
vector<lower=0, upper=1>[p] prob[K];
pi[1] = nu[1];
for(j in 2:(K-1))
{
pi[j] = nu[j] * (1 - nu[j - 1]) * pi[j - 1] / nu[j - 1];
}
pi[K] = 1 - sum(pi[1:(K - 1)]);
for (k in 1:K)
{
for (ps in 1:p)
{
prob[k, ps] = inv_logit(rates[k, ps]);
}
}
}
model {
real mix[K];
nu ~ beta(1, alpha);
for(i in 1:n)
{
for(k in 1:K)
{
mix[k] = log(pi[k]);
for (ps in 1:p)
{
mix[k] += bernoulli_lpmf(x[i, ps] | prob[k, ps]);
}
}
target += log_sum_exp(mix);
}
}
```