Hello,
Imagine that I have a model with
theta = numpyro.sample("theta", dist.MultivariateNormal(loc = mu, covariance_matrix = cov)
but when I run a MCMC/NUTS I realise that I would like to add some loose constraints like
theta in [bnds_low, bnds_high]
to avoid the sampler to spend time far on the tails.
nb. in a determinist point of view the above code would be a gaussian prior on the thetas, and to minimize a likelihood I would have used scipy.minimize with parameter bounds.
What do you suggest? Thanks