@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 ‘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… Hello, i am not sure that i understand well how the autodiagonalnormal autoguide works I have a mlp model and i want to do an svi on this model Given that my model uses pyrosample statements (that trigger pyro.sample statements), i guess that my guide has the same structure (except that i can’t add a name to these pyrosample statements as i usually do with pyro.sample) Hi, i’m working on a model where the likelihood follows a matrix normal distribution, x ~ mn_{n,p} (m, u, v)
Is there a way to implement the matrix normal distribution in pyro If i replace the conjugate priors with. Hi all, i am new to pyro and i am trying to use pyro for gpc I’d like to use some customized link functions e.g Cloglog or gev link but i didn’t find out how to do it from those tutorials Is it possible to do so using pyro
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.
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