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The classic example... $X \sim Po\left (\lambda\right ), Y \sim Po\left (\mu\right)$, X and Y are independent. Show that the conditional distribution of X is binomially distributed. Or in other words, $P(X=k\mid X+Y = n) = P (\tilde{X} = k), \tilde{X} \sim B\left (\frac{\lambda}{p} ,\frac{\lambda}{n}\right )$.

I've so far managed to reach to this step, and have been stuck since. Just somehow gotta get a $\frac{1}{n!}$ in the denominator, that would then complete the proof..or at least I think..

$$P(X=k\mid X+Y=n) = \frac{\frac{\lambda^{k}\mu^{n-k}}{k!(n-k)!}}{P(X+Y = n)}= \frac{\frac{\lambda^{k}\mu^{n-k}}{k!(n-k)!}}{\sum_{i=1}^{n} \frac{\lambda^{i}\mu^{n-i}}{i!(n-i)!}}$$

Thanks for the help!

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The summation in the denominator must run from $i=0$ to $n$. –  user17762 May 29 '12 at 19:50
    
There is a typo, you wrote $B\left (\frac{\lambda}{p} ,\frac{\lambda}{n}\right )$. Presumably you mean something like $B\left(\frac{\lambda}{\lambda+\mu},n\right)$. –  André Nicolas May 29 '12 at 20:18
    
@AndréNicolas did you mean the other way around? Isn't it represented in B(n,p)? And yep thanks for the correction! I thought the probability and n were just merely derived from the $\lambda$ without considering the $\mu$. Makes more sense now anyway! –  mercurial May 30 '12 at 6:19
    
Sure. Binomial $n$ trials prob. of success each trial $\frac{\lambda}{\lambda+\mu}$. –  André Nicolas May 30 '12 at 12:30
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HINT

Remember that $$\sum_{i=0}^{n} \binom{n}{i} \lambda^i \mu^{n-i} = \left( \lambda + \mu\right)^n$$ The above gives us that $$\sum_{i=0}^{n} \dfrac{n!}{i! (n-i)!} \lambda^i \mu^{n-i} = \left( \lambda + \mu\right)^n$$ which inturn gives us that $$\sum_{i=0}^{n} \dfrac{ \lambda^i \mu^{n-i}}{i! (n-i)!} = \dfrac{\left( \lambda + \mu\right)^n}{n!}$$

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