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Let <math>Z(t), t\ge 0</math>, be a random process obtained by switching between the values 0 and 1 according to the event times in a counting process <math>N(t)</math>. Let <math>P(Z(0)=0)=p</math> and
 
Let <math>Z(t), t\ge 0</math>, be a random process obtained by switching between the values 0 and 1 according to the event times in a counting process <math>N(t)</math>. Let <math>P(Z(0)=0)=p</math> and
  
<math>Cov(X_i,X_j) = \left\{ \begin{array}{ll}
+
<math>P(N(t)=k) =
{\sigma}^2, & i=j\\
+
\frac{1}{1+\lambda t}(\frac{\lambda t}{1+\lambda t})^k</math>
\rho{\sigma}^2, & |i-j|=1\\
+
0, & otherwise
+
  \end{array} \right.</math>
+
  
Suppose we take 2 consecutive samples from this sequence to form a vector <math>X</math>, which is then linearly transformed to form a 2-dimensional random vector <math>Y=AX</math>. Find a matrix <math>A</math> so that the components of <math>Y</math> are independent random variables You must justify your answer.
+
for <math> k = 0, 1, ...</math>. Find the pmf of <math> Z(t)</math>.
  
 
:'''Click [[ECE_PhD_QE_CNSIP_2013_Problem1.2|here]] to view student [[ECE_PhD_QE_CNSIP_2013_Problem1.2|answers and discussions]]'''
 
:'''Click [[ECE_PhD_QE_CNSIP_2013_Problem1.2|here]] to view student [[ECE_PhD_QE_CNSIP_2013_Problem1.2|answers and discussions]]'''

Revision as of 23:09, 2 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 $ be an exponential random variable with parameter $ \lambda $, so that $ f_X(x)=\lambda{exp}(-\lambda{x})u(x) $. Find the variance of $ X $. You must show all of your work.

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Part 4.

Consider a sequence of independent random variables $ X_1,X_2,... $, where $ X_n $ has pdf

$ \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]\\ &+\frac{1}{n}\sigma exp(-\sigma x)u(x)\end{align} $.

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 $ X_1,X_2,... $ converges in mean-square if and only if $ E[|X_n-X_{n+m}|] \to 0 $ as $ n \to \infty $, for every $ m>0 $.

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