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Batch processing pyro models so cc Beyond pyro’s tutorials, a popular community resource for getting started with bayesian data science is the book “statistical rethinking”, for which all code snippets have been ported to pyro and numpyro by @fehiepsi and others. @fonnesbeck as i think he’ll be interested in batch processing bayesian models anyway
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I want to run lots of numpyro models in parallel This function is fit to observed data points, one fit per object 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.
Hi all, i am coding the example from the mbml book, chapter 1 I am expecting to have samples within my mcmc, and i don’t think there is an issue with my model definition (maybe?) since i can just sample the model and obtain the correct conditioning as well as the correct answer Am i making an obvious mistake # min example of a mystery import jax import jax.numpy as jnp import numpyro.
This would appear to be a bug/unsupported feature If you like, you can make a feature request on github (please include a code snippet and stack trace) However, in the short term your best bet would be to try to do what you want in pyro, which should support this. Apologies for the rather long post
This is the gmm code that works when i fit with both hmc and svi.
Model and guide shapes disagree at site ‘z_2’ Torch.size ( [2, 2]) vs torch.size ( [2]) anyone has the clue, why the shapes disagree at some point Here is the z_t sample site in the model Z_loc here is a torch tensor wi…
I’m seeking advice on improving runtime performance of the below numpyro model I have a dataset of l objects
