User talk:Lothar.brendel: Difference between revisions
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== Florian's eCDF == | |||
Good to know that "empirical probability" is a synonym for the relative frequency. | Good to know that "empirical probability" is a synonym for the relative frequency. | ||
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--[[User:Florian|Florian]] ([[User talk:Florian|talk]]) 15:03, 10 July 2024 (CEST) | --[[User:Florian|Florian]] ([[User talk:Florian|talk]]) 15:03, 10 July 2024 (CEST) | ||
: When dropping \(n\in\mathbb N\), to ''which'' values \(n\) do you assign ficticious probabability(-density)-values and why? --[[User:Lothar.brendel|Lothar]] ([[User talk:Lothar.brendel|talk]]) 15:51, 10 July 2024 (CEST) | |||
Revision as of 14:52, 10 July 2024
Florian's eCDF
Good to know that "empirical probability" is a synonym for the relative frequency.
My own idea behind all this seemed way simpler to me at the time; although, I am not sure how to properly show that it does what it should at the moment. Basically, I conveniently forgot that the agglomerate sizes are restricted to natural numbers. From there, I can estimate the probability **density** by estimating the derivative of the CDF, in my case as discretized derivatives using the eCDF. However, this means that the values I calculated are estimates of the PDF, not of the probabilities as I originally wanted.
--Florian (talk) 14:54, 10 July 2024 (CEST)
By this logic, I am quite confused, because the PDF estimates are normalized to one. I would have expected that I need an integral for the normalization if I assumed continuous random numbers in the beginning.