Hi guys,

i am trying to model a confirmatory factor analysis on a correlation matrix. Therefore, i set up matrices and priors for loadings, factor correlations and variances and use those to calculate a model-implied correlation matrix, much like in ML estimation:

correlations_{implied} = loadings * correlations_{factor} * loadings' + variances

I struggle then to correctly set up a sampling statement. I do something like this:

```
model {
observed_correlations ~ normal(implied_correlations, 0.1);
}
```

This sampling statement seems to work, but i am concerned about the 0.1, which is just an arbitrary value i chose. Is there a more reasonable way to do this?

best, Felix