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- Timestamp:
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Jul 17, 2008, 6:41:18 PM (16 years ago)
- Author:
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buckley
- Comment:
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v1
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| | 1 | == Treatment of weights in YODA == |
| | 2 | |
| | 3 | Suggested guiding principles: |
| | 4 | |
| | 5 | * Weight treatment must allow arbitrary partitioning of data: fill1 + fill2 + fill3 == fill1 + (fill2 + fill3) == (fill1 + fill2) + fill3, etc. |
| | 6 | * The whole point of weights is that they allow more rapid convergence of distributions, but the errors must be equivalent to filling with a larger number of samples with smaller weights which add up to the same. |
| | 7 | * Negative weights must be treated carefully: filling a bin with +w and later with -w should behave as if neither fill ever occurred, ''including the error''. Hence, sum(w^2^) should be filled as sumw2 += sign(w) * w^2^. |
| | 8 | |
| | 9 | How to treat questions about bin/histo statistics where there is no weight (i.e. no information)? Throw exception? Return mean = 0, error = inf? |
