pymc-devs / pymc-devs/pymc-examples
GP Heteroskedastic
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 398
- Forks
- 325
- Avg merge
- 9d 15m
- Merged PRs (30d)
- 1
Description
File: https://github.com/pymc-devs/pymc-examples/blob/main/examples/gaussian_processes/GP-Heteroskedastic.ipynb
Reviewers:
The sections below may still be pending. If so, the issue is still available, it simply doesn't
have specific guidance yet. Please refer to this overview of updates
Changes for discussion
Changes listed in this section are up for discussion, these are ideas on how to improve
the notebook but may not have a clear implementation, or fix some know issue only partially.
ArviZ related
- Use ArviZ and xarray for posterior predictive plotting
Notes
Exotic dependencies
Computing requirements
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
Open examples/gaussian_processes/GP-Heteroskedastic.ipynb and review the notebook-updates overview first. The only proposed change is to use ArviZ and xarray for posterior predictive plotting, so clarify the intended approach before starting. Done means the notebook's posterior predictive plots use the agreed approach and still run correctly.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, python
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100