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Batch processing pyro models so cc However, in the short term your best bet would be to try to do what you want in pyro, which should support this. @fonnesbeck as i think he’ll be interested in batch processing bayesian models anyway
I want to run lots of numpyro models in parallel If you like, you can make a feature request on github (please include a code snippet and stack trace) 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.
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…
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 working on a model where the likelihood follows a matrix normal distribution, x ~ mn_{n,p} (m, u, v) M ~ mn u ~ inverse wishart v ~ inverse wishart as a result, i believe the posterior distribution should also follow a matrix normal distribution
Is there a way to implement the matrix normal distribution in pyro
If i replace the conjugate priors with. 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
Hi there, i am relatively new to numpyro, and i am exploring a bit with different features In one scenario, i am using gaussian copulas to model some variables, one of which has a discrete marginal distribution (say, bernoulli) In my pipeline, i would generally start from some latent normal distributions with a dependent structure, apply pit to transform to uniforms, then call icdf from the. This would appear to be a bug/unsupported feature
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