Starting from the example of Bürkner & Vuorre (Ordinal Regression Modells in Psychology: A Tutorial)page 12, I use ``stancode(fit_sc1)``` to retrieve the following model:

```
fit_sc1 <- brm(formula = rating ~ 1+ belief, data = stemcell, family = cumulative())
```

to understand what’s going on in Ordinal Models.

Messing with the code I changed

```
for (n in 1:N) {
target += ordered_logistic_lpmf(Y[n]| mu[n], temp_Intercept);
}
```

to

```
target += ordered_logistic_lpmf(Y| mu, temp_Intercept);
```

to try the vectorization.With the vectorization I get 10x slower code, 3951 divergences, \hat{R} \gt 4 and low ESS.

I’m totally OK with non-vectorized code… but what is happening here and what am I missing here?