Find the PDF of W when W= X + Y + Z. Random Varibles, Uniform Distrubutions.

In summary, the discussion involves finding the pdf of W, which is the sum of three independent random variables X, Y, and Z, each uniformly distributed on the interval from 0 to 1. The solution involves using Theorem 3.8.1 twice to determine the pdf of X+Y, and then integrating it with Z to obtain the desired pdf for W. The final solution is a piecewise defined function with three intervals, each with its own bounds of integration.
  • #1
Dwolfson
9
0

Homework Statement



1. Let X , Y and Z be independent random variables, uniformly distributed on the interval
from 0 to 1. Use Theorem 3.8.1 twice to find the pdf of W = X + Y + Z .

Thm. 3.8.1 States: If X & Y are continuous random varibles wth pdfs fx(x) and fy(y), respectively then

fw(w)=[tex]\int[/tex][tex]^{INF}_{-INF}[/tex] fx(x)fy(w-x) dx

Homework Equations



When setting up the problem I understand how to do it once.. I set S=X+Y and get the PDF of s to be a piece wise defined function:

Keep in mind that I am completely unsure of my bounds of integration -- I do not have any idea if these bounds are correct -- But I tried to use my best logic and intuition to figure them out.

PDF of s = s when 0[tex]\leq[/tex]s<1
PDF of s = 2-s when 1[tex]\leq[/tex]S[tex]\leq[/tex]2

The Attempt at a Solution



I figured that there will be three intervals for the final solution:

0[tex]\leq[/tex]w<1

1[tex]\leq[/tex]w<2

2[tex]\leq[/tex]w[tex]\leq[/tex]3

Some of these may be strictly < or > but that is part of my confusion...

So my intuition tells me:

I should calculate:

[tex]\int[/tex][tex]^{W}_{0}[/tex] S ds
Giving PDF of W = w^2/2 for 0[tex]\leq[/tex]w<1

After this I am very lost:

I Imagine the interval from 1 to 2 will be the sum of two integrals... [tex]\int[/tex][tex]^{w}_{1}[/tex] 2-S ds +
[tex]\int[/tex][tex]^{w-1}_{0}[/tex] S ds
which gives : W+1 as the PDF ( I Think this is incorrect by the way).

I justified my bounds by this logic:

If S is between 0 and 1 we'd use the pdf(s) that = S meaning that if W is between 1 and 2 and S is between 0 and 1 we'd integrate from S=0 to S=w-1

If S is between 1 and 2 we'd use the pdf(s) that = 2-S from 1 to W becuase S has to be at least one and less than W.

For the third interval of W is between 2 and 3:

I used this integral:

[tex]\int[/tex][tex]^{w-1}_{1}[/tex] 2-S ds

I justified my bounds by this logic:

Since W=X+Y+Z and S=X+Y S would have to be atleast 1 for this interval to make sense... Since if S=1 and Z=1 then W=2. The upper bound of this integral would be W-1 because if W is maximized at 3 then S would be 2. So for any number between 2 and 3 the upper bound is W-1.Thank You for your help,
-Derek
 
Last edited:
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  • #2
the solution:

[tex]f_w(w)=\begin{cases}
\frac{w^2}{2} & 0<w\leq 1 \\
-\frac{3}{2}+3 w-w^2 & 1<w\leq 2 \\
\frac{1}{2} (-3+w)^2 & 2<w\leq 3 \\
0 & \text{Everywhere } \text{else}
\end{cases}
[/tex]
 
  • #3
Thanks... I like to work backwards.. now I can figure out the bounds.

Appreciated.
 
  • #4
did you get it? need some more help?
 
  • #5
I did eventually figure it out.. Thank you for the followup.
 

Related to Find the PDF of W when W= X + Y + Z. Random Varibles, Uniform Distrubutions.

1. What is a PDF?

A PDF (Probability Density Function) is a mathematical function that describes the probability of a random variable falling within a certain range of values.

2. How is a PDF calculated for a random variable?

A PDF is calculated by taking the derivative of the Cumulative Distribution Function (CDF) of the random variable.

3. What are random variables?

Random variables are numerical values that are determined by chance. They can take on different values with varying probabilities.

4. What is a uniform distribution?

A uniform distribution is a probability distribution in which all outcomes are equally likely. This means that the probability of a variable falling within a certain range is the same for all values in that range.

5. How can the PDF of W be found when W= X + Y + Z?

The PDF of W can be found by taking the convolution of the PDFs of X, Y, and Z. This involves integrating the product of the individual PDFs over all possible values of the variables.

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