arviz-devs / arviz-devs/bayesian-workflow
Incomplete sleep_study case study
- Dominant language
- R
- Stars
- 12
- Forks
- 0
- PR merge metrics
- No merged PRs in 30d
Description
* There are a few sub-sections that explicitly use an LKJ for correlated priors. https://github.com/bambinos/bambi/issues/702
* Exgaussian and lognormal families are not implemented in Bambi. Should we implement them in Bambi or define them in the case study
Contributor guide
No contributing guide indexed for this repository
Research direction
Start by reading the sleep_study case study sections that use LKJ priors for correlated parameters and review the linked Bambi issue #702. Check whether Exgaussian and lognormal families are available in Bambi, then determine whether the case study should define them or wait for Bambi support. Done means the missing sections and family-handling decision are resolved in the case study.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100