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coming back to this package after a few years away and pleased to see that Bayesian inference in R remains as janky as I remember it (jkjk). I have a simple question: does `fixtype = "vb_full"` work i…
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Most of the more popular probabilistic programming languages have implementations of variational inference (VI). As such, it's absence in monad-bayes is something of an obstacle to real-world use.
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Here is to discuss about the design concerning variational inference methods.
So far in AugmentedGaussianProcesses.jl things are done this way:
- There are two functions `∇E_μ` and `∇E_Σ` which r…
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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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We allow `model.fit(method="advi")`, but we have largely ignored what happens next for the user. We should improve this situation. We should at least improve two aspects what object we return when us…
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@torfjelde the BNN tutorial is failing because
```julia
update(q, (μ, exp.(ω)))
```
doesn't seem to work anymore, because `update` doesn't seem to be exported anymore. I tried calling
```…
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References:
http://papers.nips.cc/paper/2172-vibes-a-variational-inference-engine-for-bayesian-networks.pdf
http://www.jmlr.org/papers/volume6/winn05a/winn05a.pdf
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To support VI we need to decide what different aspect we should implement.
## Data structure
We probably need a special data structure for VI results.
It could contain:
- posterior mean
- p…
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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 …