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### Describe the issue:
There are a couple of issues with the current design:
i) The keys of `start` and `start_sigma` are inconsistent as can be seen in example below.
ii) I can specify arbitrar…
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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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Despite merging #118 , [`pygnme`](https://github.com/BoothGroup/pygnme) is still not playing nicely with the CI and so the tests aren't being run automatically. This could lead to issues down the road…
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One of the tests for the inside_outside method is `test_sample_as_parent_fails`. Can we actually date samples that have descendant undated nodes using the current `variational_gamma` implementation, o…
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My data dimensions are [B, N, D], the first dimension is batchsize, the second dimension is the sequence length in the sample, and the third dimension is the feature channel.
Before feeding into the…
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### Describe the workflow you want to enable
Currently, there is no included implementation of a PCA algorithm made for handling binary data in the scikit-learn library. However, the algorithm for …
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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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**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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As discussed today, it may be a good idea to incorporate some specialized/customized minimizers for variational circuits.
Here I leave some references where they are introduced or benchmarked.
[…