Hello,

I am very new to numpyro, and I was reading the tutorials to solve my specific problem.

In particular, in the eight-school example, the tutorial mentioned the `LocScaleReparam`

to generate `N(mu, sigma)`

from `N(0, 1) * sigma + mu`

.

I was wondering how to construct such transformation when it is for a multivariate normal distribution. Suppose the parameter of interest is a 2-dim `mu_vector`

with a multivariate normal prior.

There is a linear relationship between the likelihood function and `mu_vector`

such that

X ~ MVN(T*mu_vector, Sigma)

where `T`

is a known matrix of size `qx2`

, q>2.

Do I need to apply `numpyro.handlers.reparam`

to reconstruct the random variable?

`with numpyro.handlers.reparam(config={'theta': TransformReparam()}): theta = numpyro.sample( 'theta', dist.TransformedDistribution(dist.Normal(0., 1.), dist.transforms.AffineTransform(mu, tau)) ) numpyro.sample('obs', dist.Normal(theta, sigma), obs=y)`