**Background**: I have a simulation model which has unobserved parameters. I created a metamodel using artificial neural networks (ANN) because the simulation model takes a lot of time. I am trying to estimate the unobserved parameters using Bayesian calibration, where priors are based on current knowledge, and the likelihood of observing data is being estimated from the metamodel.

**Query**: I have two random variables X and Y for which I am trying to get the posterior distribution using STAN. The prior distribution of X is uniform, *U(0,2)*. The prior for Y is also uniform, but it will always exceed X i.e., *Y ~ U(X,2)*. Since Y depends on X, how can I define the prior distribution for Y in STAN such that the constraint Y>X holds? I am new to STAN, so I would appreciate any suggestions or guidance on how to proceed. Thank you so much!

```
// Xq is the vector of parameters for calibration
parameters {
matrix<lower=-1, upper=1>[num_targets,num_inputs] Xq;
}
```