(New page: Category:ECE Category:QE Category:CNSIP Category:problem solving Category:random variables Category:probability <center> <font size= 4> [[ECE_PhD_Qualifying_Exams|...) |
(Entered question 1 statement) |
||
(6 intermediate revisions by 2 users not shown) | |||
Line 22: | Line 22: | ||
---- | ---- | ||
==Question== | ==Question== | ||
− | ''' | + | '''Problem 1. ''' 25 pts |
+ | Consider a random experiment in which a point is selected at random from the unit square (sample space <math>\mathcal{S} = [0,1] \times [0,1] </math>). Assume that all points in <math>\mathcal{S} </math> are equally likely to be selected. Let the random variable <math>\mathbf{X}(\omega)</math> be the distance from the outcome <math>\omega</math> to the nearest edge (i.e. the nearest point on one of the four sides) of the unit square. | ||
+ | '''(a)''' Find the c.d.f. of <math>\mathbf{X}</math>. Draw a graph of the c.d.f.. | ||
− | + | '''(b)''' Find the p.d.f. of <math>\mathbf{X}</math>. Draw a graph of the p.d.f.. | |
+ | '''(c)''' What is the probability that <math>\mathbf{X}</math> is less than 1/8? | ||
+ | :'''Click [[ECE-QE_CS1-2012_solusion-1|here]] to view student [[ECE-QE_CS1-2012_solusion-1|answers and discussions]]''' | ||
+ | ---- | ||
− | :'''Click [[ECE-QE_CS1- | + | '''Problem 2. ''' 25 pts |
+ | |||
+ | |||
+ | State and prove the Chebyshev inequality for random variable <math>\mathbf{X}</math> with mean <math>\mu</math> and variance <math>\sigma^2</math>. In constructing your proof, keep in mind that <math>\mathbf{X}</math> may be either a discrete or continuous random variable. | ||
+ | |||
+ | |||
+ | |||
+ | :'''Click [[ECE-QE_CS1-2012_solusion-1|here]] to view student [[ECE-QE_CS1-2012_solusion-1|answers and discussions]]''' | ||
---- | ---- | ||
− | ''' | + | '''Problem 3.''' 25 pts |
+ | |||
+ | |||
+ | Let <math class="inline">\mathbf{X}_{1} \dots \mathbf{X}_{n} \dots </math> be a sequence of independent, identical distributed random variables, each uniformly distributed on the interval [0, 1], an hence having pdf | ||
+ | <br> | ||
+ | <math class="inline">f_{X}\left(x\right)=\begin{cases} | ||
+ | \begin{array}{lll} | ||
+ | 1, \text{ for } 0 \leq x \leq1\\ | ||
+ | 0, \text{ elsewhere. } | ||
+ | \end{array}\end{cases}</math> | ||
+ | <br> | ||
+ | |||
+ | Let <math class="inline">\mathbf{Y}_{n}</math> be a new random variable defined by | ||
+ | <br> | ||
+ | <math class="inline">\mathbf{Y}_{n} = min \,\{{ \mathbf{X}_1, \mathbf{X}_2, \dots \mathbf{X}_n} \}</math> | ||
+ | <br> | ||
+ | |||
+ | '''(a)''' Find the pdf of <math class="inline">\mathbf{Y}_{n}</math>. | ||
+ | '''(b)''' Does the sequence <math class="inline">\mathbf{Y}_{n}</math> converge in probability? | ||
− | + | '''(c)''' Does the sequence <math class="inline">\mathbf{Y}_{n}</math> converge in distribution? If yes, specify the cumulative function of the random variable it converges to. | |
− | </math> | + | |
− | :'''Click [[ECE-QE_CS1- | + | :'''Click [[ECE-QE_CS1-2012_solusion-2|here]] to view student [[ECE-QE_CS1-2012_solusion-2|answers and discussions]]''' |
---- | ---- | ||
[[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 20:36, 5 August 2018
Communication, Networking, Signal and Image Processing (CS)
Question 1: Probability and Random Processes
August 2012
Question
Problem 1. 25 pts Consider a random experiment in which a point is selected at random from the unit square (sample space $ \mathcal{S} = [0,1] \times [0,1] $). Assume that all points in $ \mathcal{S} $ are equally likely to be selected. Let the random variable $ \mathbf{X}(\omega) $ be the distance from the outcome $ \omega $ to the nearest edge (i.e. the nearest point on one of the four sides) of the unit square.
(a) Find the c.d.f. of $ \mathbf{X} $. Draw a graph of the c.d.f..
(b) Find the p.d.f. of $ \mathbf{X} $. Draw a graph of the p.d.f..
(c) What is the probability that $ \mathbf{X} $ is less than 1/8?
- Click here to view student answers and discussions
Problem 2. 25 pts
State and prove the Chebyshev inequality for random variable $ \mathbf{X} $ with mean $ \mu $ and variance $ \sigma^2 $. In constructing your proof, keep in mind that $ \mathbf{X} $ may be either a discrete or continuous random variable.
- Click here to view student answers and discussions
Problem 3. 25 pts
Let $ \mathbf{X}_{1} \dots \mathbf{X}_{n} \dots $ be a sequence of independent, identical distributed random variables, each uniformly distributed on the interval [0, 1], an hence having pdf
$ f_{X}\left(x\right)=\begin{cases} \begin{array}{lll} 1, \text{ for } 0 \leq x \leq1\\ 0, \text{ elsewhere. } \end{array}\end{cases} $
Let $ \mathbf{Y}_{n} $ be a new random variable defined by
$ \mathbf{Y}_{n} = min \,\{{ \mathbf{X}_1, \mathbf{X}_2, \dots \mathbf{X}_n} \} $
(a) Find the pdf of $ \mathbf{Y}_{n} $.
(b) Does the sequence $ \mathbf{Y}_{n} $ converge in probability?
(c) Does the sequence $ \mathbf{Y}_{n} $ converge in distribution? If yes, specify the cumulative function of the random variable it converges to.
- Click here to view student answers and discussions