Undergrad Conditional distribution of geometric series

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The discussion focuses on finding the conditional distribution P(x_1 = k | x_1 + x_2 = n) for independent variables x_1 and x_2, where x_1 follows a geometric distribution with parameter p and x_2 with parameter 1-p. The initial attempt at the probability distribution was deemed incorrect, leading to a revised approach using the joint probability Q_k, which combines the probabilities of x_1 and x_2. The correct conditional probability P_k is formulated as the ratio of Q_k to the total probability of x_1 + x_2 = n. This method emphasizes the importance of calculating the denominator accurately to derive the desired conditional distribution. The discussion highlights the complexities involved in working with geometric distributions and conditional probabilities.
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Can someone help me on this question? I'm finding a very strange probability distribution.

Question: Suppose that x_1 and x_2 are independent with x_1 ~ geometric(p) and x_2 ~ geometric (1-p). That's x_1 has geometric distribution with parameter p and x_2 has geometric distribution with parameter 1-p.

Find the distribution of P(x_1 = k| x_1 + x_2 = n)


I found P^-k (1-p)^k-1(2p-1)/1-(1-p)^n-1, but that's certainly wrong.
 
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How about let ##Q_k=P((x_1=k)\cap (x_2=n-k))=P(x_1=k)P(x_2=n-k)=p^{n-k}(1-p)^k##, then desired prob. ##P_k=\frac{Q_k}{\sum_{j=0}^nQ_j}##. The denominator is ##P(x_1+x_2=n)##.
 
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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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