I’m trying to reparameterize truncated distributions. I have tried to use `LocScaleReparam`

but the truncated distributions are not left with a `loc`

parameter.

More generally put, I am trying to constrain variables to be between 0 and 1 but sampled from a normal. So far I’ve tried to just sample from the normal and apply `jnp.clip`

to the samples, and also wrapping the distributions with `dist.TruncatedDistribution`

.

The model I am trying to adapt from a stan implementation defines a lower and upper bound when defining the variables. Is there perhaps a numpyro equivalent to writing `vector<lower=0, upper=1>[N] latent_var_name;`

in stan?

I’ve tried to use the truncated distributions (which works) but then the only sensible reparameterization I found I could use was neural transport and the SVI training of the guide does not really converge.

Does anyone have ideas on this one? Would appreciate any input.