Conditional Distribution of Multinomial Random Variables

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The discussion focuses on deriving the conditional distribution of a multinomial random variable Y given the sum Y_1 + Y_2 = m. It is established that this conditional distribution is binomial with parameters n = m and π = π_1/(π_1 + π_2). Participants express difficulty in applying conditional probability definitions to the multinomial case, noting that similar results hold for Poisson random variables. The conversation highlights the challenge of transitioning from Poisson to multinomial distributions in this context. Ultimately, the thread seeks clarification on the correct approach to this problem.
broegger
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I've been staring at this for hours. Any hints?

Let the vector Y = (Y_1,Y_2,\dots,Y_k) have a multinomial distribution with parameters n and \pi = (\pi_1,\pi_2,\dots,\pi_k):

\sum_{i=1}^{k}Y_i = n, \quad \sum_{i=1}^{k}\pi_i = 1​

Show that the conditional distribution of Y_1 given Y_1+Y_2=m is binomial with n = m and \pi = \frac{\pi_1}{\pi_1+\pi_2}.

I've tried to apply the definition of a conditional probability and sum over the relevant events in the multinomial distribution, but it gives me nothing.

Thanks.
 
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Hmmm, this works if they are Poisson. Not sure if it works if it is multinomial. The standard way to do this would be to compute P[X_1=x|X_1+Y_2=m], as you tried.

Edit: I should be more precise. If Y_1 and Y_2 poisson r.v. with paramaters lambda1 and lambda2, then Y_1 | Y_1+Y_2=m is distributed as Binomial(m, lambda1/(lambda1+lambda2)).
 
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