Graduate LogLikelihood - Poisson distribution

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The discussion revolves around fitting parameters of particle data using log-likelihood minimization with a Poisson distribution. The user notes that higher pulse amplitudes yield lower Poisson probabilities, which contradicts their importance in the analysis. They seek advice on correcting this effect to ensure that higher amplitudes are appropriately weighted in the likelihood function. The comparison being made involves the number of measured photoelectrons versus expected values. Suggestions for addressing the issue are requested from the community.
Zuzana
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Hello :)
I try to fit some parameters of the particle (e.g. energy, direction) be means of log-likelihood minimization.
Input data to likelihood function are pulses amplitudes, while Poisson distribution is used. However, the problem is that Poisson distribution is as follows
1661160531826.png

i.e. for higher pulse amplitute there is a lower Poisson probability and thus higher likelihood value. However, pulses with higher amplitudes are very important in the event and the probability for them should be higher, not? Please, do you know how to correct this effect? Or what would you suggest to do?

Thank you very much in advance.
 
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What are you comparing?
 
mathman said:
What are you comparing?
number of photoelectrons. measured vs expected
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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