do you need to know the noise model to make the noise model?
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Description
we assume we know something, namely a white noise model specified by ivar maps, to get properly weighted map differences.
how hard would it be to generalize this, eg first to a strawman model? would this help anything (eg noise from map differences vs. autos minus crosses)?
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Research direction
Start by reviewing how ivar maps currently provide the white-noise model for weighted map differences. Compare that assumption with the proposed strawman model and with noise estimates from map differences versus autos minus crosses. Done should mean a defined generalization path and a clear assessment of whether it improves the noise model.
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Assessment
- Tech stack
- python
- Domain
- data
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100