Presumably you want to add some kind of likelihood, too?
Will that just be distributions for data so that there's no
Jacobian adjustment required for the constraining transform?
If so, this probably won't be so hard --- we replace the default
uniform on the unconstrained params with a multivariate normal or
Student-t and then turn off the Jacobian.
The only trick would be specifying the prior parameters. And of
course plumbing this through the sampler.
We're also going to be exposing the transforms, so another way to
go would be to have this, which is what you have to do now:
data {
int<lower=0> N;
vector[N] mu_theta_raw;
cov_matrix[N] Sigma_theta_raw;
parameters {
vector[N] theta_raw;
}
transformed parameters {
... define params theta in terms of theta_raw using transforms ...
}
model {
theta_raw ~ multi_normal(mu_theta_raw, Sigma_theta_raw);
... likelihood ...
}
But it's still going to be ugly because of slicing theta_raw into
segments.
- Bob
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