The law of total probability with extra conditioning

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SUMMARY

The discussion centers on the law of total probability with extra conditioning, specifically addressing the need for the events \(A_i\) to form a partition of the sample space \(E\). Participants clarify that when all probabilities are conditional on \(E\), it simplifies the equation, treating \(E\) as the universal set. The proof hinges on ensuring that the \(A_i\) events partition \(E\) correctly, which is essential for applying the theorem accurately.

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red65
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TL;DR
the proof of a theorem
Hello, I am studying probability and came across this theorem, it's the law of total probability with extra conditioning, I tried to work out a proof but couldn't ,does anyone know the proof for this :
1672964293581.png

thanks!
 
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1) If you are looking for a proof, you should be very careful about the exact statement. There is more to that statement, right? Don't the ##A_i## need to be a partitioning?
2) If all probabilities are conditional on ##E##, isn't that just another probability where ##E## is the universe and does not need to be included in the notation?
 
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red65 said:
TL;DR Summary: the proof of a theorem

Hello, I am studying probability and came across this theorem, it's the law of total probability with extra conditioning, I tried to work out a proof but couldn't ,does anyone know the proof for this :
View attachment 319864
thanks!
That's just the usual equation with a restriction to ##E## as the universal set or sample space. Given the proviso, as above, that the ##A_i## (when restricted to ##E##) partition ##E##.
 
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