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Hi, i’m trying to construct a bayesian structural time series model with just a local linear trend and a quarterly seasonality The inference is working but i am not sure is it even possible to. I’m pretty sure i have the model specification set up correctly but it’s giving really bad results when training using svi on some simulated data
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I’m not sure what could be wrong here, maybe autonormal isn’t a good guide for this type of time series model. Assume we don’t have normal distribution likelihood and instead of numpyro.sample(., obs=x) i used the numpyro.factor I am running nuts/mcmc (on multiple cpu cores) for a quite large dataset (400k samples) for 4 chains x 2000 steps
I assume upon trying to gather all results
(there might be some unnecessary memory duplication going on in this step?) are there any “quick fixes” to reduce the memory footprint of mcmc Following the sgt example i set my observed within the scan using handlers.condition, this works well for a standard distribution but fails for a transformed distribution Is it possible with nuts to set the sample for an observed, transformed distribution within a scan Import numpy as np import numpyro import numpyro.distributions as dist from numpyro.contrib.control_flow import scan from.
Hello i was wondering if it’s possible to parallelize the number of particles used to estimate the elbo over multiple gpus (i.e Num_particles=40, and if we have 4 gpus, then each gpu will estimate the elbo from 10 particles, for each sample from the training set, and aggregate at the end) I’ve seen some topics discussing multi gpu distributed pyro.plate training, but i’m more. I have tried to control the dependence/indepence of dimensions of the latent variables but still failed
Could you please provide some suggestions about how to solve it
Pyro provides access to the pytorch schedulers, and the pyro clippedadam also has a specific learning rate decay parameter I can not find anything of the sort in numpyro, however, or any example that does this? Hi, i’m using the latest pyro and tutorials In another place i have a bvae pytorch implementation that trains on audio waveforms and denoises them by losing information during reconstruction
The training step is as f… Hi all, i have a model with custom likelihood function Is it possible to use runtime utils predictive to draw sample for posterior predictive tasks To pass it later to arviz
Here is a simple example
