Comments (10)
FYI: LKJ in tfp now has Cholesky parameterization tensorflow/probability@9b007d8
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Ok, well for now (until there is a bit of consensus), I proceed as was done before, referencing the issue in the PR. Don't want to step on any toes if I'm just starting to contribute 😄, but I do like the idea of opening smaller issues and referencing those in the PR.
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LKJ in tfp is the same as LKJCorr.
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Update: #46 merges in some more random variables (those that can be created via tfp's bijectors and transformed variables). There are still quite a few random variables outstanding, though.
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@eigenfoo your PR gives me a template to work off! I'll see how fast I can get setup and running for development at work.
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Edited!
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I haven't contributed yet but would be interested to have a go with helping with some of these. Looking at the code base it seems a lot of these still are not implemented, so it is still open, correct?
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@tblazina yes, still open! I recently learned a new pattern at this year's PyCon, which helps with tracking progress. Was wondering what the other devs think of this - when putting in a PR, put in also an issue that we can close, and reference it in the branch name (e.g. distribution-flat-#93
)? I think there's github magic that we can take advantage of 😄
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@ericmjl, ok great, thanks for quick response! I'll try to get dev environment all set up and see if I can get plugging away at some of this. I think your idea about tracking progress sounds to me and was along the lines of my next question, is that for a big issue like this with lots of different parts, is the best practice in this project to submit a PR for each distribution that we implement and simply reference this issue in the PR?
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is the best practice in this project to submit a PR for each distribution that we implement and simply reference this issue in the PR?
That is how I would have done it before this year's PyCon. This kind of issue is like an over-arching one, which multiple other smaller issues can reference. Hence, I think it's more appropriate to reference the smaller issues, so that you can take advantage of the auto-closing keywords (e.g. "resolves #391")
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Related Issues (20)
- No transformations for Bounded Distributions
- Class level dimension and parameter information HOT 1
- Backport support for coords from pymc3
- Add log likelihood to arviz trace objects
- `sample_prior_predictive` and `sample_posterior_predictive` issues with `use_auto_batching=False` HOT 2
- Write a notebook on manual model vectorization HOT 1
- Mass matrix adaptation sampling code in notebooks fails HOT 1
- Error in transformed variables with manual model vectorization HOT 4
- Make pm.sample work with AR distribution HOT 6
- Changes in PR #271 don't work with BatchStacker HOT 2
- PyMC3 vs PyMC4 for a new project? HOT 2
- [Question] Is there a timeline or roadmap for PyMC4? HOT 2
- Numerical instabilities with tf.float32 for cholesky decomposition HOT 3
- ValueError: Cannot create a tensor proto whose content is larger than 2GB. HOT 2
- Connecting pymc4 and diffeqpy for differential equations HOT 1
- Sampling fails when `num_chains=1` HOT 5
- PYMC4.Bound not found HOT 3
- Add support for LocalLinearTrend and Seasonal HOT 1
- calculation with tensor run errors HOT 2
- ERROR (aesara.graph.opt): Optimization failure due to: constant_folding HOT 1
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