MHB Joint Probability Distributions

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The discussion revolves around the confusion regarding the calculation of joint probability distributions. The initial formula used, P_{(\xi1,\xi2)}(x1,x2)=P_{(\xi1)}(x1)P_{(\xi2)}(x2), applies only to independent variables. The user realized that their variables were not independent, which clarified their misunderstanding. This highlights the importance of recognizing variable independence when calculating joint distributions. Understanding these concepts is crucial for accurate probability analysis.
Carla1985
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I have this question:

View attachment 690and I'm a little confused. To calculate joint distributions in the earlier questions i was using:P_{(\xi1,\xi2)}(x1,x2)=P_{(\xi1)}(x1)P_{(\xi2)}(x2)But that would mean that if:P_{(\xi1,\xi2)}(2,0)=0\ either\ P_{(\xi1)}(2)=0\ or\ P_{(\xi2)}(0)=0which can't be true in either case as P_{(\xi1,\xi2)}(1,0)\ isnt\ 0\ and\ neither\ is\ P_{(\xi1,\xi2)}(2,1)Can someone please explain what I'm missing. Thanks :/
 

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Nevermind, I found my mistake. This was only for independent variables, which these obviously aren't :)
 
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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