Any reason you couldn’t use a weighted mixture of a centered and non-centered parameter to try to improve mixing?

Eg

```
data{
int n;
}
parameters{
vector[n] theta;
vector[n] theta_norms;
real theta_mu;
real<lower=0> theta_sigma;
vector[n] p; // Mixture model weight
}
transformed parameters{
vector[n] theta_nc;
vector[n] theta_use;
theta_nc = theta_mu + theta_sigma*theta_norms; // NC
theta_use = p .* theta + (rep_vector(1.0, n) - p).*theta_nc;
model {
theta ~ normal(theta_mu, theta_sigma); // Centered
theta_norms ~ std_normal(); // Non-centered
}
```

It’s not a perfect fix - you’ll inherit some part of the worst sampling.

But - to use a very loose analogy - 50% bad might still give much higher effective sample size than picking the 100% bad option