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The current tutorials cover a majority of MCMC. Could we get one for variational inference? The edward tutorial on Supervised Learning shows how to run inference using Kullback-Leibler divergence. It …
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https://arxiv.org/abs/2002.00643
https://twitter.com/LucaAmb/status/1359561091278381056
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[arxiv paper](https://arxiv.org/abs/1610.05683)
looping in @naesseth
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**Notebook title**: Variational Inference: Bayesian Neural Networks
**Notebook url**: https://www.pymc.io/projects/examples/en/latest/variational_inference/bayesian_neural_network_advi.html
## Iss…
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Hey all,
would be nice if there was an implementation of [Automatic structured variational inference](https://arxiv.org/abs/2002.00643) in NumPyro (unless it is already there and I am not seeing it…
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# variational_inference | GoGoGogo!
Variational Inference: A Review for Statisticians General Problem Setting: Consider a joint density of latent variables \(\mathbf{z} = z_{1:m}\) and observations \…
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Hello,
I'm trying to follow the tutorial. I used the "multiome_congas_object" provided and segments_selector_congas keeps failing.
Since I'm using your provided example data, I shouldn't have this…
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#### Summary:
I would like to use the ADVI implemented in Stan but can't find helpful docs.
#### Description:
My model needs to fit a distribution based on KL divergence. This is possible with va…
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Two things.
First thing: a suggestion to mention in the intro some of the other myriad names that point to nearly-identical ideas to neural SBI, such as NPE (neural posterior estimation) and FAVI (…