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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
How to calculate probability as correlation matrix? - Tutorials - Pyro . . . We just want to calculate the co-occurrence probability of features (output like a correlation matrix, Input is an M*N matrix (M features, N samples) From our experience, we have known the distribution might be learned through pyro Could you kindly tell us which tutorial or code can solve a similar problem, with a pyro style method? Your advice is curial for us Thanks
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
Accessing warmup chains - numpyro - Pyro Discussion Forum Hello! How can I access warmup chains in numpyro? Here’s part of my code to show how I’ve set collect_warmup=True: from numpyro import distributions as dist, infer sampler = infer MCMC( infer NUTS(model), num_warmup=num_warmup, num_samples=num_samples, num_chains=num_chains, progress_bar=True) # (Attempt to) save warmup chains sampler warmup(jax random PRNGKey(0), time_axis, Y_unc, Y
Mixture model with discrete data in Numpyro - Pyro Discussion Forum Hi Apologies for the rather long post I am trying to modify the code from the Gaussian mixture model tutorial (I have also pulled bits of code from various other posts on this forum and elsewhere - if you recognise your code, thank you very much!) to compute a mixture model where the data is Bernoulli distributed categorical This is the GMM code that works when I fit with both HMC and SVI