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Batch processing pyro models so cc This function is fit to observed data points, one fit per object @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 have a dataset of l objects 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.
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%) Could you help me understand why the loss improves while performance doesn’t, and suggest potential fixes Import torch import pyro import pyro. 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… Hello, i am new to numpyro, so please bear with me
A few year ago i wrote in stan a spatiotemporal model for analysing climate extremes
Recently, i decided to translate such model to numpyro to see if it would run faster (using nuts) When i set “num_chains=1”, the model runs indeed 3x faster (on cpu) in numpyro and the results are identical to those in stan, which is great I’m seeking advice on improving runtime performance of the below numpyro model
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