Timeline for Choose best binning for binned maximum likelihood fit?
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
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| Feb 23, 2022 at 22:56 | comment | added | dan | Binning is necessary mostly due to computation of bin-by-bin uncertainties taking all correlations into account. Besides, with the "expectation" distributions being statistically limited (I am using a MC simulation), the predictions at the tails of the distribution are likely to have large statistical uncertainties. At least at the edges, I would need to have reasonably large-width bins to have a reliable signal and background prediction. | |
| Feb 23, 2022 at 6:17 | comment | added | Closed Limelike Curves | Binning for any reason other than computational speed is a bad idea; use as many bins as you can. Each bin should have an equal number of observations to minimize the amount of information you lose by binning. | |
| Feb 23, 2022 at 0:49 | history | edited | kjetil b halvorsen♦ |
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| Feb 23, 2022 at 0:48 | comment | added | kjetil b halvorsen♦ | If the reason for binning is computational speed, why not just take the max number of bins you can afford? | |
| S Feb 22, 2022 at 22:56 | review | First questions | |||
| Feb 23, 2022 at 2:48 | |||||
| S Feb 22, 2022 at 22:56 | history | asked | dan | CC BY-SA 4.0 |