Add WALNUTS (Within-Orbit Adaptive Leapfrog No-U-Turn Sampler) Implementation
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 2.8k
- Forks
- 315
- Avg merge
- 3d 9h
- Merged PRs (30d)
- 27
Description
Feature Summary
Add a support for the algorithm presented in The Within-Orbit Adaptive Leapfrog No-U-Turn Sampler
Why is this needed?
It seems to have some improvements over NUTS:
Empirical evaluations on multiscale target distributions, including Neal’s funnel and the Stock-Watson
stochastic volatility time-series model, demonstrate that WALNUTS achieves substantial improvements in
sampling efficiency and robustness compared to standard NUTS.
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
No source file, test, or entry point is named. Start by reading the linked WALNUTS paper and locating NumPyro’s existing NUTS and inference implementations. Done means WALNUTS is integrated as a supported sampler with coverage demonstrating the requested algorithm.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- numpy, python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100