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It seems numpyro can model latent integers using funsor, great This works with minor modifications (priors from a beta. In this example i want to model 2 latent groups using dirichlet and categorical
I am struggling with the ii variable, which needs to be length n=100 The traces of cluster_proba jump back and forth between 0 and 1 during sampling when the clusters are about equally probable I tried various shape parameters but i failed
I previously got this to work in pymc3 but am curious how to do it in numpyro
# pip install funsor import jax.numpy as np import. Apologies for the rather long post This is the gmm code that works when i fit with both hmc and svi. I’m seeking advice on improving runtime performance of the below numpyro model
I have a dataset of l objects This function is fit to observed data points, one fit per object Hi, i’m trying to learn multiple (5) coefficients for each treatment group So i set up my code so that for each “treatment” in the “treatment” plate, i draw 5 numbers from a normal distribution independently
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, 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 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. I think i am doing the log_prob calculation correctly as the two methods produce the same values for the same data, but when i try and fit the model using mcmc i don’t get anything like sensible results
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