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When i want to conduct inference on my model using svi, i use the “init_to_mean” strategy Hi there, i’m building a model which is related to the scanvi pyro example for modeling count data while learning discrete clusters for data, and i’m having an issue with the. My understanding is that all parameters are initialized to their mean, and if they don’t.

Hi there, i am relatively new to numpyro, and i am exploring a bit with different features I think i am doing the log_prob calculation correctly as the two methods produce the same values for the same data, but when i try and fit the model using mcmc i don’t get anything. In one scenario, i am using gaussian copulas to model some variables, one of which has.

I was curious if pyro would easily enable putting a gaussian mixture model (gmm) as the prior on the latent space of a vae

I took the vae tutorial code and changed the model to the. I saw that pyro is planning to add at least a truncated normal distribution soon However i want to implement a truncated normal distribution as prior for a sample param 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. I’m seeking advice on improving runtime performance of the below numpyro model I have a dataset of l objects For each object, i sample a discrete variable c.

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