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I know that for the sum of independent rv's the PMF is a convolution

so...

Ʃ(1/k)(1/n-k) from k = 1 to L

but I'm wondering.. can this be simplified?

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- Thread starter magnifik
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In summary, the conversation is about finding the probability mass function of Z, which is the sum of two independent uniform discrete random variables, X and Y. The PMF is a convolution, but the provided formula is incorrect. The correct formula is still unknown.

- #1

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I know that for the sum of independent rv's the PMF is a convolution

so...

Ʃ(1/k)(1/n-k) from k = 1 to L

but I'm wondering.. can this be simplified?

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Lines of constant Z=z have a slope of -x, and the probability of Z will be the number of discrete points intersected by a constant

try drawing it, this should also help understand the analytic convolution method

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RGV

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Ray Vickson said:

RGV

For a sum of two independent uniform discrete random variables, Z = X + Y, what is the probability mass function of Z? X and Y both take on values between 1 and n

I know that for the sum of independent rv's the PMF is a convolution

so...

Ʃ(1/k)(1/n-k) from k = 1 to n

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magnifik said:For a sum of two independent uniform discrete random variables, Z = X + Y, what is the probability mass function of Z? X and Y both take on values between 1 and n

I know that for the sum of independent rv's the PMF is a convolution

so...

Ʃ(1/k)(1/n-k) from k = 1 to n

That is not the required convolution. I have no idea what it is.

RGV

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