Factor Analysis/PPCA Tutorial
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Tutorials/Examples
- Dominant language
- Python
- Stars
- 2.8k
- Forks
- 315
- Avg merge
- 3d 9h
- Merged PRs (30d)
- 27
Description
I would like to write a short tutorial on using numpyro for factor analysis and probabilistic principal components analysis (PPCA), following the exposition in Murphy (2012) chapter 12. Opening an issue to solicit related requests and feedback from the maintainers and the community prior to jumping in!
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 by reading Murphy (2012), chapter 12, and the existing NumPyro tutorial structure. The finished work should be a short tutorial explaining factor analysis and probabilistic principal components analysis using NumPyro, with enough exposition and examples to follow that chapter.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100