control scale of initialization randomness
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- Dominant language
- Python
- Stars
- 207
- Forks
- 28
- Avg merge
- 7d 21h
- Merged PRs (30d)
- 1
Description
I would like to be able to control the scale of randomization around init_mean. In Stan, we often find that something like independent standard normal initialization leads you way into the tails in high dimensions (e.g., in logistic regressions with hundreds or thousands of covariates) and reducing the scale of randomization can help immensely in getting off the ground.
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Research direction
No file or test is named. Start by locating the initialization path that handles init_mean and confirm how random values are generated; then trace how a scale option could be exposed. Done means callers can reduce or increase initialization randomness around init_mean, with behavior checked for high-dimensional models.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Active
- Clarity
- Mostly clear
- Newbie friendliness
- 64/100