Hello, I’m fairly new to Stan so apologies if I’ve missed something obvious.

I have the following model that I would like to generate samples from in the generated quantities block:

```
model {
target += alpha*multi_normal_lpdf(y | mu, Sigma);
// priors
mu[1] ~ normal(-4,10);
mu[2] ~ normal(25,10);
Sigma ~ wishart(nu,v);
}
```

It doesn’t seem possible to use the multi_normal_rng function as that would not appropriately consider the influence of alpha. I can generate samples from this model easily enough in r using rejection sampling with the estimated parameters, but I would like to be able to do it directly from Stan if possible. Here’s my full code:

```
// The input data is a vector 'y' of length 'N'.
data {
int<lower=0> N;
vector[2] y[N];
}
// The parameters accepted by the model.
parameters {
vector[2] mu; //means
vector<lower=0>[2] sigma; //sds
real<lower = -1,upper = 1> rho;//correltation
}
transformed parameters{
cov_matrix[2] Sigma; // create first correlation matrix
real alpha;
real nu; // wishart prior degrees of freedom
cov_matrix[2] v; // wishart prior covariance matrix
Sigma[1,1] = square(sigma[1]); // variance
Sigma[2,2] = square(sigma[2]); // varance
Sigma[1,2] = rho*sigma[1]*sigma[2]; // covariance
Sigma[2,1] = Sigma[1,2]; // covariance
alpha = .05; // helpfulness -- will be estimated later but set to .05 for testing
nu = 5;
v[1,1] = 5; // variance 1
v[2,2] = 5; // variance 2
v[1,2] = 0; // covariance
v[2,1] = 0;
}
// The model to be estimated. We model the output
// 'y' to be log bivariate normally distributed with mean 'mu'
// and standard deviation 'sigma' multiplied by 'alpha'.
model {
target += alpha*multi_normal_lpdf(y | mu, Sigma);
// priors
mu[1] ~ normal(-4,10);
mu[2] ~ normal(25,10);
Sigma ~ wishart(nu,v);
}
```

Any help would be greatly appreciated!