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Help post，shape problem - Pyro Discussion Forum with pyro plate("data", x size(0)): pyro sample("obs", dist Normal(mu, sigma), obs=y squeeze(-1),infer={"scale": annealing_factor}) #obs，真实数据+噪声
Batch processing numpyro models using Ray - forum. pyro. ai Hello again, Related post: 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
Model and guide shapes disagree at site - Pyro Discussion Forum 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 hellip;
Reducing MCMC memory usage - numpyro - Pyro Discussion Forum I am running NUTS MCMC (on multiple CPU cores) for a quite large dataset (400k samples) for 4 chains x 2000 steps mcmc run actually ran until the end, but then died with an out-of-memory exception; 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? For
Implementation normalizing flow in matrix normal distribution Hi, I’m working on a model where the likelihood follows a matrix normal distribution, X ~ MN_{n,p} (M, U, V) I’m using conjugate priors: 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
Icdf for discrete distributions - numpyro - Pyro Discussion Forum 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
Extra sampling site in manual guide compared to model - numpyro - Pyro . . . i see 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
Denoising VAE - Tutorials - Pyro Discussion Forum 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 hellip;