Comments (6)
Quick comment, the situation
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I believe so, @will-cern can presumably confirm.
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So following up on some discussion points raised by @cranmer from our NYU/UW-Madison group meeting discussion:
- Incorrect is probably too strong a word here given that if your likelihood function has this shape then you are probably not in the regieme that using the asymptotics is a good idea and you should be using pseudoexperiments, where you are free to choose whatever test statistic you'd like.
A question that I have is if you believe that you would be in a situation where (edit)
from pyhf.
I agree that "incorrect" is not a good description, maybe "inconsistent with the definition" is better. The question about the usefulness of that approach in such a scenario stands of course.
Regarding your question:
from pyhf.
Sorry, you're both obviously correct, but I was typing #2352 (comment) while trying to listen to another meeting and I typoed. I meant to write
A question that I have is if you believe that you would be in a situation where
$\mu < 0$
(so not
but if I had read better I would have seen that @alexander-held addressed this in the first sentence:
For models with some physically motivated bounds (like
$\mu\geq0$ )
so we're already starting there with the answer to my question.
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@alexander-held For reference, as you mention in your 2023-10-16 ATLAS Statistics Committee Meeting slides this was addressed in
xRooFit: fixed recently and deployed in StatAnalysis
I'm assuming this is https://gitlab.cern.ch/will/xroofit/-/commit/ebf6a49194c3c08dd2e12d8933fe16067b6243e8 (xRooFit
doesn't use MR based workflow so going by commit message)?
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