strategies for prior rescaling with cross-validation
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- R
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Description
@avehtari @dpsimpson @bgoodri here's a summary of our discussion:
Option 1
- Use MAD instead of SD for rescaling
- Need to handle case when MAD is 0
Option 2
- Use SD for everything (including binary variables)
- Then need to do prior-weighted version of LOO
Our hunch was that option 1 is a good approximation to option 2 but we should do experiments.
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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
The issue names no files, tests, or entry points. Start by locating the prior-rescaling and cross-validation implementations in the R package, then review how MAD, SD, binary variables, and prior-weighted LOO are currently handled. Done means experiments compare both options and document a selected strategy, including behavior when MAD is zero.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- r
- Domain
- data
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 15/100