I am estimating a cumulative odds model and I have a set of cut-points/intercepts that are ordered. I am using a t(3, 0, 5) prior, which seems to work quite well. Is it advisable (and more importantly, is it even possible) to use non-centered parameterization in this case?

I have L levels in the response, and there are K groups, each of which has its own cut-points. The current parameterization is

```
parameters {
...
ordered[L-1] c[K]; // (K X [L-1] matrix)
...
}
model {
...
for (k in 1:K)
c[k] ~ student_t(3, 0, 5);
...
}
```

I attempted to use the gamma(3/2, 3/2) and standard normal parameterization, but the structured/ordered nature of this parameter doesn’t seem to translate well under this transformation. Again, I am wondering if it is even possible or advisable to attempt this.