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This is my first time using pyro so i am very excited to see what i can built with it.🙂 specifically, i am trying to do finite dirichlet process clustering with variational inference I have pyro’s stable release, are staticsvi and lbfgs implemented in the pyro’s developers release I want to generalize this into a chinese restaurant process involving an “infinite” number of states

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However, i believe this can be recreated (and could be a nice example notebook) using pyro’s discretehmm distribution and use of the infer_discrete function Thanks so much for your reply I am having trouble inferring the latent state for each observation (emission).

Hi, i am using nuts to fit a multivariate normal model (dim=12) on a dataset

I noticed that when running nuts, the cpu usage is very high (600% cpu) with one chain I increased the number of chains to six, the cpu usage decreased (to 130% cpu), however, the running time had a significant increase I thought the reason might be because of the dataset So i run the lkj.py in pyro/examples.

I was curious if pyro would easily enable putting a gaussian mixture model (gmm) as the prior on the latent space of a vae I took the vae tutorial code and changed the model to the following using mixturesamefamily It runs (on mnist) but after 30 or so epochs i get nans I wanted to confirm this implementation is correct in principle before trying to debug the numerical issues

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. Dear developers and other pyro experts, i have been trying to set up an hmc/mcmc/nuts sampling routine with pyro, specifically by providing my own likelihood function that ‘describes’ the posterior distribution but is not constructed by a combination of torch distributions Until now i have not been able to figure out how to solve my issues, and there are little examples that come close to.

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.

I saw that pyro is planning to add at least a truncated normal distribution soon However i want to implement a truncated normal distribution as prior for a sample param I came accross the rejector distribution and thought this could maybe provide a solution Def __init__(self, loc, scale_0, max_x0)

I am looking for regressions and classification examples for simple bayesian (probabilistic) neural network Can you point me to these sources?