tensorflow / tensorflow/probability
Implement (folded) rank normalized R hat
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Description
Reference [3] in the potential_scale_reduction documentation recommends rank normalization (section 4.1) and folded rank normalization (section 4.2) for better assessing convergence in the presence of heavy tails.
I'd like to contribute implementing this. Would an extra argument in tfp.mcmc.potential_scale_reduction in the spirit of split_chains be a good choice?
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start at the tfp.mcmc.potential_scale_reduction API and read sections 4.1 and 4.2 of Reference [3] in the linked documentation. Clarify with maintainers whether rank normalization and folded rank normalization belong behind an extra argument like split_chains; done means the agreed API supports both convergence assessments.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- tensorflow
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 30/100