Conditional distribution of geometric series

In summary, the conversation discusses finding the distribution of P(x_1 = k| x_1 + x_2 = n) given two independent geometric distributions with parameters p and 1-p. The suggested solution involves calculating Q_k and then using it to find the desired probability P_k.
  • #1
user158675
1
0
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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  • #2
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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1. What is the formula for calculating the conditional distribution of a geometric series?

The formula for calculating the conditional distribution of a geometric series is P(X=k|X≥1) = (1-p)^k * p, where p is the probability of success and k is the number of trials.

2. How is the conditional distribution of a geometric series different from the unconditional distribution?

The conditional distribution of a geometric series takes into account a specific condition, such as the number of trials, while the unconditional distribution considers all possible outcomes without any conditions.

3. Can the conditional distribution of a geometric series be used to predict future outcomes?

Yes, the conditional distribution of a geometric series can be used to predict future outcomes based on the given condition and the probability of success.

4. How can the conditional distribution of a geometric series be applied in real-life situations?

The conditional distribution of a geometric series can be applied in various real-life situations, such as predicting the number of trials needed to achieve a certain number of successes in a series of events, or calculating the probability of success in a given condition.

5. Are there any limitations to using the conditional distribution of a geometric series?

One limitation is that it assumes independence between each trial, which may not always be the case in real-life situations. Additionally, the condition used in the calculation may not always accurately represent the actual scenario, leading to inaccurate predictions.

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