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[[Category:ECE]]
 
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[[Category:QE]]
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[[Category:CE]]
 
[[Category:problem solving]]
 
[[Category:problem solving]]
[[Category:random variables]]
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[[Category:algorithms]]
[[Category:probability]]
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<font size= 4>
Communication, Networking, Signal and Image Processing (CS)
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Computer Engineering(CE)
  
Question 1: Probability and Random Processes
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Question 1: Algorithms
 
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Let <math>\lambda = \frac{1}{\mu}</math>, then <math>E(X)=E(Y)=\frac{1}{\lambda}</math>.
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===Solution 3===
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For this problem, it is very useful to note that for any independent random variables <math>X</math> and <math>Y</math> and their characteristic functions <math>\phi_X(\omega),\,\phi_Y(\omega)</math> we have the following property:
  
 
<math>
 
<math>
\phi_{X+Y}=E[e^{it(X+Y)}]=\int_{X}\int_{Y}e^{it(X+Y)}p(x,y)dxdy
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\phi_{X+Y}(\omega) = \phi_X(\omega)\phi_Y(\omega)
 
</math>
 
</math>
  
As X and Y are independent
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We then note that the characteristic function of an exponential random variable <math>Z</math> is written as
  
 
<math>
 
<math>
\phi_{X+Y}=\int_{X}\int_{Y}e^{it(X+Y)}p(x)p(y)dxdy
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\phi_Z (\omega) = \frac{\lambda}{\lambda - i\omega}
 
</math>
 
</math>
  
----
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where <math>\lambda</math> parameterizes the exponential distribution. As such, we can write the characteristic function of <math>X+Y</math> as
[[ECE-QE_CS1-2015|Back to QE CS question 1, August 2015]]
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 +
<math>
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\phi_{X+Y}(\omega) = \phi_X(\omega)\phi_Y(\omega) \\
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= \left(\frac{\lambda}{\lambda-i\omega}\right)^2
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</math>
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Next, we recall that the mean of an exponential random variable is equal to the inverse of its parameter, i.e. <math>\frac{1}{\lambda}</math>. Then the above expression becomes
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 +
<math>
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\phi_{X+Y}(\omega) = \left(\frac{\frac{1}{\mu}}{\frac{1}{\mu}-i\omega}\right)^2
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</math>
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Multiplying by <math>\frac{\mu^2}{\mu^2}</math> gives
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<math>
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\phi_{X+Y}(\omega) = \left(\frac{1}{1-i\omega\mu}\right)^2
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</math>
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[[ECE-QE_CE1-2015|Back to QE CE question 1, August 2015]]
  
 
[[ECE_PhD_Qualifying_Exams|Back to ECE Qualifying Exams (QE) page]]
 
[[ECE_PhD_Qualifying_Exams|Back to ECE Qualifying Exams (QE) page]]

Latest revision as of 22:00, 7 March 2016


ECE Ph.D. Qualifying Exam

Computer Engineering(CE)

Question 1: Algorithms

August 2015


Solution 3

For this problem, it is very useful to note that for any independent random variables $ X $ and $ Y $ and their characteristic functions $ \phi_X(\omega),\,\phi_Y(\omega) $ we have the following property:

$ \phi_{X+Y}(\omega) = \phi_X(\omega)\phi_Y(\omega) $

We then note that the characteristic function of an exponential random variable $ Z $ is written as

$ \phi_Z (\omega) = \frac{\lambda}{\lambda - i\omega} $

where $ \lambda $ parameterizes the exponential distribution. As such, we can write the characteristic function of $ X+Y $ as

$ \phi_{X+Y}(\omega) = \phi_X(\omega)\phi_Y(\omega) \\ = \left(\frac{\lambda}{\lambda-i\omega}\right)^2 $

Next, we recall that the mean of an exponential random variable is equal to the inverse of its parameter, i.e. $ \frac{1}{\lambda} $. Then the above expression becomes

$ \phi_{X+Y}(\omega) = \left(\frac{\frac{1}{\mu}}{\frac{1}{\mu}-i\omega}\right)^2 $

Multiplying by $ \frac{\mu^2}{\mu^2} $ gives

$ \phi_{X+Y}(\omega) = \left(\frac{1}{1-i\omega\mu}\right)^2 $


Back to QE CE question 1, August 2015

Back to ECE Qualifying Exams (QE) page

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