I want to set the varying ordered vector, the model like this:

```
data {
int<lower=1> N; // Number of observations
int<lower=1> K; // Number of ordinal categories
int<lower=1> D;
array[N] int<lower=1, upper=K> y; // Observed ordinals
matrix[N,D] x;
int g[N];
int<lower=1> P; //P different threshold
}
parameters {
vector[D] beta;
ordered[K-1] alpha; // (Internal) cut points
vector[K-1] yita[P-1];
real<lower=0,upper=10> sigma;
}
transformed parameters{
ordered[K - 1] thresh[P];
thresh[1] = alpha;
for (i in 2:P){
thresh[i] = thresh[i-1]+ yita[i-1];
}
}
model {
beta~ normal(0,10);
for (i in 1: (P-1))
yita[i]~ normal(0,sigma);
alpha ~ normal(0,10);
for (i in 1:N)
y[i]~ ordered_logistic(x[i]*beta,thresh[g[i]]);
}
```

I want to construct varying `thresh`

. By adding a random variable `yita`

. But how can I control the order? When sampling I have an error like this:

```
Rejecting initial value:
Chain 1: Error evaluating the log probability at the initial value.
Chain 1: Exception: model26c023f6ff1__namespace::log_prob: thresh[sym1__] is not a valid ordered vector. The element at 3 is 4.3765, but should be greater than the previous element, 4.45567 (in 'string', line 67, column 2 to column 27)
Chain 1: Rejecting initial value:
```

Sometimes the four chains can find the initial values, and I can get a convergent result. And sometimes I have only two chains that can get initial values. I think if there is a method to control I can get efficient sampling every time.