Help with probability distribution function question

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SUMMARY

The discussion focuses on finding the probability density functions for the sum and product of two independent random variables, X and Y, both uniformly distributed over the interval [0, a]. For the sum X + Y, the resulting density function is uniformly distributed with a probability density of 1/(2a). For the product X * Y, the density function is constant at 1/a², as the variables can be visualized as points within a square of side length a. The key takeaway is the distinction between uniform distributions and other types such as Gaussian, which affects the approach to solving these problems.

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Homework Statement



Let X and Y be two independent random variables with the same probability density funtion over:

f(x) = {1/a if x € [0,a]
{0 if x=0

Find the density distribution of a) X + Y and b) X*Y


Homework Equations





The Attempt at a Solution



Ok, my initial thoughts are for:

a) That f(X+Y) is simply the product of each of their densities, which would result in 1/(a^2)

b) f(X*Y) is the sum of their individual distributions. i.e. 2/a


This seems a little too simple for me and I think I am looking at it from the wrong perspective.

Any thoughts or advice?
 
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No, i don't think this is right. Just consider that the two random variables are idependently and identically distributed w.r.t the distribution you mentioned above. So [itex]X + Y[/itex]takes its values in the interval [itex][0, 2a][/itex]. What you were referring to is the Gaussian distribution where the result concerning a would be valid. In this case however, since the points are distributed according to (1) a continuous distribution which is (2) uniform, we conclude that if the two random variable [itex]X, Y[/itex] also are distributed according to this distribution, then there sum has to be distributed (again uniformly) with probability density [itex]1/2a[/itex]. Converesely, take question b. You may regard X and Y as some sort of random coordinates which give you point lying a sqaure with side length a. Again, recalling that we deal with uniform distributions, we find the probability density function to be constant [itex]1/a^{2}[/itex]. The problem is that one has to distinguish between uniform distributions and the standard distributions, i.e., Gaussian, binomial, geometric, multinomial, Poisson, exponential.
 
You can get the density f(z) of X+Y by differentiating the CDF F(z) = Pr{X+Y <= z}, and the latter is an integral over the two-dimensional region {x+y <= z, 0 <= x,y <= a}. Or, Google "convolution".

RGV
 

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