centering in autocorr
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- Dominant language
- Python
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Description
The mean of x is subtracted before calculating the fft. However, the sample mean is ofcourse a biased estimate - especially if we have short chains. But we have a less biased estimate of the mean of a parameter if we use the mean from all chains+walkers. It should be slightly better for too short chains.
(An alternative idea is to calculate the ACF and the tau from the first-differences - Then there would be no need to center).
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 locating the autocorrelation and FFT implementation, then read the existing tests around chain centering and integrated autocorrelation time. Compare the current per-chain sample mean with a mean across chains and walkers, and assess the proposed first-difference alternative. Done means the chosen approach is implemented, covered by tests, and its effect on short chains is demonstrated.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100