Hello,

similarly to a Mixture Model of Gaussians with joint probability for Clustering/Classification, I am trying to find a Mixture Model with joint probability but with von Mises distribution.

I have two input data sets which together describe a point, so I guess I have to use a bivariate von Mises distribution, but there is no such ‘multivariate’ function implemented in stan. Nevertheless, is there a way to implement something like this?

Right now I have code inspired from my tests programming with a Gaussian distribution and for only one data set

```
data {
int K; // number classes
int N; // number of all data points
vector[N] y;
}
parameters{
vector [K] mu;
vector<lower=0, upper=90>[K] kappa;
simplex [K] weights;
}
model{
for (n in 1:N) {
vector [K] pb;
for (k in 1:K) {
pb[k] = log(weights[k]) + von_mises_lpdf(y[n] | mu[k], kappa[k]);
}
target += log_sum_exp (pb);
}
}
```

I believe I also then have an array of vectors for my two input data sets

```
vector[2] y[N];
```

Does anyone have an idea how to realise this?