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.