Find the PDF of W = X + Y + Z on a Uniform Distribution

AI Thread Summary
The discussion revolves around finding the probability density function (pdf) of W = X + Y + Z, where X, Y, and Z are independent and uniformly distributed on (0,1). The user has successfully derived the pdf of S = X + Y, identifying it as piecewise defined for different intervals. They are now attempting to find the pdf of W by integrating over the appropriate intervals, specifically struggling with the intervals for 1 < W < 2 and 2 < W < 3. The thread includes references to convolution for combining the distributions and seeks further guidance on the integration process. The conversation highlights the complexities of deriving the pdf for sums of independent uniform random variables.
Dwolfson
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I am stumped.

I have that W=X+Y+Z and that S=X+Y

These are all X, Y, & Z and Independent and Uniformly Distributed on (0,1)

I found the pdf of S to be (Assume all these < rep. less than or equal to):

S when 0<S<1
2-S when 0<S<1

So I continued:

To do pdf of S+Z=W

I figured there will be 3 intervals:

when 0<W<1, 1<W<2, and 2<W<3:

I Have figured out the one from 0<W<1

to be integral from 0 to W pdf(w)=S(pdf(W-S))ds

= W^2/2

For the other two intervals I am struggling on which pdf of S to use and what is the interval of integration..

Thank you in advance for your help,
--Derek
 
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so the sum of 2 RVs is given by their convolution, in particular the square pulse integral
http://en.wikipedia.org/wiki/Convolution

so for S = X+Y, with p_X(X=x), \ p_Y(Y=y)
p_S(s) = \int dx p_X(x) p_Y(s-x)

similarly, it should just follow that for W = S + Z
p_W(w) = \int dz p_Z(z) p_S(w-z)
 
Dwolfson said:
I am stumped.

I have that W=X+Y+Z and that S=X+Y

These are all X, Y, & Z and Independent and Uniformly Distributed on (0,1)

I found the pdf of S to be (Assume all these < rep. less than or equal to):

S when 0<S<1
2-S when 0<S<1

and i assume you mean
2-S when 1<S<2
 
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