| Version 1 (modified by , 17 years ago) (diff) |
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Treatment of weights in YODA
Suggested guiding principles:
- Weight treatment must allow arbitrary partitioning of data: fill1 + fill2 + fill3 == fill1 + (fill2 + fill3) == (fill1 + fill2) + fill3, etc.
- 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.
- 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(w2) should be filled as sumw2 += sign(w) * w2.
How to treat questions about bin/histo statistics where there is no weight (i.e. no information)? Throw exception? Return mean = 0, error = inf?
