Init_to_mean functionality

When I want to conduct inference on my model using SVI, I use the “init_to_mean” strategy. My understanding is that all parameters are initialized to their mean, and if they don’t have a mean, the “init_to_median” strategy will be used. However, when I inspect out the value of a parameter during this initialization phase, it is not as is expected. For example, I would expect a parameter with a Normal(0.0, 1.0) prior to be initialized to 0.0, but I’m seeing this is not the case actually. Here is a minimal working example.

import jax
import numpyro
import numpyro.distributions as dist
from numpyro.infer.autoguide import AutoDelta
from numpyro.infer import init_to_mean, SVI, Trace_ELBO
from numpyro.optim import Adam

def model():
    numpyro.sample("x", dist.Normal(0, 1))

# Set up AutoDelta with init_to_mean
guide = AutoDelta(model, init_loc_fn= init_to_mean())
optimizer = Adam(0.01)
svi = SVI(model, guide, optimizer, loss=Trace_ELBO())

rng_key = jax.random.PRNGKey(0)
svi_state = svi.init(rng_key)
# Access raw param value for x
print("Initial point estimate for x:", svi.get_params(svi_state))

What was your output? For me, your code worked as expected:

Screenshot 2025-05-02 at 12.25.13 PM

I suppose you’re using an older version of NumPyro, i.e. numpyro<0.18.0? I think this PR fixed the issue. Specifically, please see this line.

Yes, that must be it. I’m on 0.16.1, because that version is compatible with the nvidia jax container i’m using nvcr.io/nvidia/jax:24.10-py3. Thanks for the help! I’ll need to update the container and the version of numpyro as well.