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Hello pyro community, i’m trying to build a bayesian cnn for mnist classification using pyro, but despite seeing the elbo loss decrease to around 10 during training, the model’s predictive accuracy remains at chance level (~10%) I am trying to use lognormal as priors for both Could you help me understand why the loss improves while performance doesn’t, and suggest potential fixes

Import torch import pyro import pyro. There is another prior (theta_part) which should be centered around theta_group Batch processing pyro models so cc

@fonnesbeck as i think he’ll be interested in batch processing bayesian models anyway

I want to run lots of numpyro models in parallel I created a new post because This post uses numpyro instead of pyro i’m doing sampling instead of svi i’m using ray instead of dask that post was 2021 i’m running a simple neal’s funnel. As part of that, i am working with the eight schools mcmc example, but can not figure out how to save the output

I know elsewhere pyro.get_param_store() is. Hello, i’m new to pyro/numpyro and trying to wrap my head around various details, including but not limited to shapes I’m getting the below error for my model You can use `numpyro.util.format_shapes` utility to check shapes at all sites of your model.

Hello, first off, amazing job on pyro

At the moment, i sample a guide trace for each desired posterior predictive sample, replay the model with the guide trace, and sample once from it, like this Ppc = [] dummy_obs = torch.zeros((1,self.d)) for sample in range(n_samples) Would using a gradient descent optimizer like adam (eg from optax) to initialize the guess starting point for nuts be useful Is something like this already implemented in numpyro

I’m finding that the time to convergence for my nuts inference is very sensitive to how small my uncertainties are that go into my gaussian. Hi everyone, i am very new to numpyro and hierarchical modeling

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