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'''Part 3.'''
 
'''Part 3.'''
  
Let <math>X</math> be an exponential random variable with parameter <math>\lambda</math>, so that <math>f_X(x)=\lambda{exp}(-\lambda{x})u(x)</math>. Find the variance of <math>X</math>. You must show all of your work.
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Let <math>X</math> and  <math>Y</math> be independent identically distributed exponential random variables with mean <math>\mu</math>. Find the characteristic function of <math>X+Y</math>.  
  
:'''Click [[ECE_PhD_QE_CNSIP_2013_Problem1.3|here]] to view student [[ECE_PhD_QE_CNSIP_2013_Problem1.3|answers and discussions]]'''
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:'''Click [[ECE_PhD_QE_CNSIP_2015_Problem1.3|here]] to view student [[ECE_PhD_QE_CNSIP_2015_Problem1.3|answers and discussions]]'''
 
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'''Part 4.'''
 
'''Part 4.'''
  
Consider a sequence of independent random variables <math>X_1,X_2,...</math>, where <math>X_n</math> has pdf
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Consider a sequence of independent and identically distributed random variables <math>X_1,X_2,... X_n</math>, where each <math>X_i</math> has mean <math>\mu = 0</math> and variance <math> \sigma^2</math>. Show that for every <math>i=1,...,n</math> the random variables <math>S_n</math> and <math>X_i-S_n</math>, where <math>S_n=\sum_{j=1}^{n}X_j</math> is the sample mean, are uncorrelated.
  
<math>\begin{align}f_n(x)=&(1-\frac{1}{n})\frac{1}{\sqrt{2\pi}\sigma}exp[-\frac{1}{2\sigma^2}(x-\frac{n-1}{n}\sigma)^2]\\
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:'''Click [[ECE_PhD_QE_CNSIP_2015_Problem1.4|here]] to view student [[ECE_PhD_QE_CNSIP_2015_Problem1.4|answers and discussions]]'''
&+\frac{1}{n}\sigma exp(-\sigma x)u(x)\end{align}</math>.
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Does this sequence converge in the mean-square sense? ''Hint:'' Use the Cauchy criterion for mean-square convergence, which states that a sequence of random variables <math>X_1,X_2,...</math> converges in mean-square if and only if <math>E[|X_n-X_{n+m}|] \to 0</math> as <math>n \to \infty</math>, for every <math>m>0</math>.
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:'''Click [[ECE_PhD_QE_CNSIP_2013_Problem1.4|here]] to view student [[ECE_PhD_QE_CNSIP_2013_Problem1.4|answers and discussions]]'''
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[[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 23:17, 3 December 2015


ECE Ph.D. Qualifying Exam

Communication, Networking, Signal and Image Processing (CS)

Question 1: Probability and Random Processes

August 2015



Question

Part 1.

If $ X $ and $ Y $ are independent Poisson random variables with respective parameters $ \lambda_1 $ and $ \lambda_2 $, calculate the conditional probability mass function of $ X $ given that $ X+Y=n $.

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Part 2.

Let $ Z(t), t\ge 0 $, be a random process obtained by switching between the values 0 and 1 according to the event times in a counting process $ N(t) $. Let $ P(Z(0)=0)=p $ and

$ P(N(t)=k) = \frac{1}{1+\lambda t}(\frac{\lambda t}{1+\lambda t})^k $

for $ k = 0, 1, ... $. Find the pmf of $ Z(t) $.

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Part 3.

Let $ X $ and $ Y $ be independent identically distributed exponential random variables with mean $ \mu $. Find the characteristic function of $ X+Y $.

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Part 4.

Consider a sequence of independent and identically distributed random variables $ X_1,X_2,... X_n $, where each $ X_i $ has mean $ \mu = 0 $ and variance $ \sigma^2 $. Show that for every $ i=1,...,n $ the random variables $ S_n $ and $ X_i-S_n $, where $ S_n=\sum_{j=1}^{n}X_j $ is the sample mean, are uncorrelated.

Click here to view student answers and discussions

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