Did I correctly find the probability density function?

It doesn't seem to.. but when I plug in a number for z, it gives me a result..In summary, using characteristic functions, we can find the probability density function of Z = X + Y, where X and Y are independent random variables with probability density functions of e^-x and e^y respectively. The probability density function of Z is given by fZ(z) = 1/(2π) ∫(e^-x * e^y * e-jωz) dω, where the integral is taken from -∞ to +∞. However, this integral does not converge, so the correct probability density function of Z is undefined.
  • #1
r19ecua
17
0

Homework Statement


A random variable x has a probability density function given by

fX(x) = e-x , x ≥ 0

and an independent random variable Y has a probability density function of

fY(y) = ey , y ≤ 0

using the characterisic functions, find the probability density function of Z = X + Y

Homework Equations



[itex]\Phi[/itex]X([itex]\omega[/itex]) = E[ejωx] = ∫fX(x)ejωxdx

[itex]\Phi[/itex]Z(ω) = [itex]\Phi[/itex]X([itex]\omega[/itex]) * [itex]\Phi[/itex]Y([itex]\omega[/itex])

fZ(z) = 1/ (2[itex]\pi[/itex]) ∫ [itex]\Phi[/itex]Z(ω) * e-jωz

the integrals within section 2 are from negative infinity to positive infinity . . .
also it's 1 over 2 pi . . didn't know how to make that into a fraction :confused:

The Attempt at a Solution



My attempt was scanned and uploaded via photobucket.. here's the scan!
http://i359.photobucket.com/albums/oo39/r19ecua/scan0356.jpg

I'm wondering if Cauchy's was a good way to handle this..
 
Last edited:
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  • #2
r19ecua said:

Homework Statement


A random variable x has a probability density function given by

fX(x) = e-x , x ≥ 0

and an independent random variable Y has a probability density function of

fY(y) = ey , y ≤ 0

using the characterisic functions, find the probability density function of Z = X + Y

Homework Equations



[itex]\Phi[/itex]X([itex]\omega[/itex]) = E[ejωx] = ∫fX(x)ejωxdx

[itex]\Phi[/itex]Z(ω) = [itex]\Phi[/itex]X([itex]\omega[/itex]) * [itex]\Phi[/itex]Y([itex]\omega[/itex])

fZ(z) = 1/ (2[itex]\pi[/itex]) ∫ [itex]\Phi[/itex]Z(ω) * e-jωz

the integrals within section 2 are from negative infinity to positive infinity . . .
also it's 1 over 2 pi . . didn't know how to make that into a fraction :confused:

The Attempt at a Solution



My attempt was scanned and uploaded via photobucket.. here's the scan!
http://i359.photobucket.com/albums/oo39/r19ecua/scan0356.jpg

I'm wondering if Cauchy's was a good way to handle this..

Since ##Z## is a real-valued random variable its pdf ##f_Z(z)## cannot be complex for real ##z .## So your solution is incorrect.
 
  • #3
I just realized that I did not replace the 1's with j's which led to my grand error! The j's are meant to cancel.. my answer should be:

ez / 2 .. .. Is that correct?
 
  • #4
r19ecua said:
I just realized that I did not replace the 1's with j's which led to my grand error! The j's are meant to cancel.. my answer should be:

ez / 2 .. .. Is that correct?

No, obviously not. Does e^z have a finite integral when you integrate z from -∞ to +∞?
 

1. What is a probability density function (PDF)?

A probability density function (PDF) is a mathematical function that describes the likelihood of a random variable taking on a specific value or range of values. It is a way to represent the probability distribution of a continuous random variable.

2. How do I know if I have correctly found the probability density function?

You can check if you have correctly found the probability density function by ensuring that it satisfies two conditions: 1) the function is non-negative for all values, and 2) the area under the curve is equal to 1. You can also compare your results with known PDFs for similar variables.

3. Can a probability density function have negative values?

No, a probability density function cannot have negative values. This is because the function represents the probability of a random variable taking on certain values, and probability cannot be negative.

4. What is the difference between a probability density function and a probability mass function?

A probability density function is used for continuous random variables, while a probability mass function is used for discrete random variables. The probability density function gives the probability of a random variable taking on a range of values, while the probability mass function gives the probability of a random variable taking on a specific value.

5. How can I use a probability density function in my research or experiments?

A probability density function can be used in your research or experiments to understand the distribution of a continuous random variable and make predictions about the likelihood of certain values occurring. It can also be used to calculate probabilities and make statistical inferences.

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