EECS 126 Probability and Random Processes University of California, Berkeley: Fall 2014 Kannan Ramchandran September 23, 2014.

Similar documents
Midterm Exam 1 Solution

(Practice Version) Midterm Exam 2

Midterm Exam 1 (Solutions)

EECS 126 Probability and Random Processes University of California, Berkeley: Fall 2014 Kannan Ramchandran November 13, 2014.

EECS 126 Probability and Random Processes University of California, Berkeley: Spring 2017 Kannan Ramchandran March 21, 2017.

Midterm Exam 2 (Solutions)

EECS 126 Probability and Random Processes University of California, Berkeley: Spring 2018 Kannan Ramchandran February 14, 2018.

EECS 126 Probability and Random Processes University of California, Berkeley: Spring 2018 Kannan Ramchandran February 14, 2018.

EECS 126 Probability and Random Processes University of California, Berkeley: Spring 2015 Abhay Parekh February 17, 2015.

MATH 407 FINAL EXAM May 6, 2011 Prof. Alexander

Course: ESO-209 Home Work: 1 Instructor: Debasis Kundu

EECS 70 Discrete Mathematics and Probability Theory Fall 2015 Walrand/Rao Final

Notes on Continuous Random Variables

Page Max. Possible Points Total 100

FINAL EXAM: Monday 8-10am

1. Let X be a random variable with probability density function. 1 x < f(x) = 0 otherwise

ECE 302 Division 2 Exam 2 Solutions, 11/4/2009.

1 Basic continuous random variable problems

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Electrical Engineering and Computer Science

Class 26: review for final exam 18.05, Spring 2014

1 Basic continuous random variable problems

MAT 271E Probability and Statistics

FINAL EXAM: 3:30-5:30pm

Midterm 2. Your Exam Room: Name of Person Sitting on Your Left: Name of Person Sitting on Your Right: Name of Person Sitting in Front of You:

Notice how similar the answers are in i,ii,iii, and iv. Go back and modify your answers so that all the parts look almost identical.

STAT 311 Practice Exam 2 Key Spring 2016 INSTRUCTIONS

ECE 302 Division 1 MWF 10:30-11:20 (Prof. Pollak) Final Exam Solutions, 5/3/2004. Please read the instructions carefully before proceeding.

Random Variables Example:

2. Suppose (X, Y ) is a pair of random variables uniformly distributed over the triangle with vertices (0, 0), (2, 0), (2, 1).

University of Illinois ECE 313: Final Exam Fall 2014

Things to remember when learning probability distributions:

BINOMIAL DISTRIBUTION

CMSC Discrete Mathematics SOLUTIONS TO FIRST MIDTERM EXAM October 18, 2005 posted Nov 2, 2005

EE376A - Information Theory Final, Monday March 14th 2016 Solutions. Please start answering each question on a new page of the answer booklet.

MAT 271E Probability and Statistics

STAT 516 Midterm Exam 3 Friday, April 18, 2008

Raquel Prado. Name: Department of Applied Mathematics and Statistics AMS-131. Spring 2010

Chapter 2: Random Variables

EXAM. Exam #1. Math 3342 Summer II, July 21, 2000 ANSWERS

STAT 516 Midterm Exam 2 Friday, March 7, 2008

Math 447. Introduction to Probability and Statistics I. Fall 1998.

This exam is closed book and closed notes. (You will have access to a copy of the Table of Common Distributions given in the back of the text.

Random Variables. Definition: A random variable (r.v.) X on the probability space (Ω, F, P) is a mapping

Principles of Mathematics 12

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Electrical Engineering and Computer Science

UC Berkeley, CS 174: Combinatorics and Discrete Probability (Fall 2008) Midterm 1. October 7, 2008

Discrete Distributions

MATH 151, FINAL EXAM Winter Quarter, 21 March, 2014

STAT 414: Introduction to Probability Theory

6.041/6.431 Spring 2009 Quiz 1 Wednesday, March 11, 7:30-9:30 PM. SOLUTIONS

SOLUTIONS IEOR 3106: Second Midterm Exam, Chapters 5-6, November 8, 2012

Exercises in Probability Theory Paul Jung MA 485/585-1C Fall 2015 based on material of Nikolai Chernov

ECEN 5612, Fall 2007 Noise and Random Processes Prof. Timothy X Brown NAME: CUID:

ECE 302, Final 3:20-5:20pm Mon. May 1, WTHR 160 or WTHR 172.

CS 70 Discrete Mathematics and Probability Theory Spring 2018 Ayazifar and Rao Final

This exam is closed book and closed notes. (You will have access to a copy of the Table of Common Distributions given in the back of the text.

Math Bootcamp 2012 Miscellaneous

STAT 418: Probability and Stochastic Processes

2. Variance and Covariance: We will now derive some classic properties of variance and covariance. Assume real-valued random variables X and Y.

EE376A - Information Theory Midterm, Tuesday February 10th. Please start answering each question on a new page of the answer booklet.

Massachusetts Institute of Technology

Bernoulli and Binomial Distributions. Notes. Bernoulli Trials. Bernoulli/Binomial Random Variables Bernoulli and Binomial Distributions.

18.05 Exam 1. Table of normal probabilities: The last page of the exam contains a table of standard normal cdf values.

Stochastic Models in Computer Science A Tutorial

Discrete Mathematics and Probability Theory Fall 2014 Anant Sahai Note 15. Random Variables: Distributions, Independence, and Expectations

STAT/MA 416 Midterm Exam 2 Thursday, October 18, Circle the section you are enrolled in:

Math 493 Final Exam December 01

Physics Exam I

SUMMARY OF PROBABILITY CONCEPTS SO FAR (SUPPLEMENT FOR MA416)

CS 70 Discrete Mathematics and Probability Theory Summer 2016 Dinh, Psomas, and Ye Final Exam

Chapter 4 : Discrete Random Variables

CSE 312 Foundations, II Final Exam

MATH 3510: PROBABILITY AND STATS July 1, 2011 FINAL EXAM

Lecture Notes 2 Random Variables. Discrete Random Variables: Probability mass function (pmf)

This exam contains 6 questions. The questions are of equal weight. Print your name at the top of this page in the upper right hand corner.

Final. Fall 2016 (Dec 16, 2016) Please copy and write the following statement:

Lectures on Elementary Probability. William G. Faris

Math 218 Supplemental Instruction Spring 2008 Final Review Part A

Week 6, 9/24/12-9/28/12, Notes: Bernoulli, Binomial, Hypergeometric, and Poisson Random Variables

Lesson One Hundred and Sixty-One Normal Distribution for some Resolution

MATH 3670 First Midterm February 17, No books or notes. No cellphone or wireless devices. Write clearly and show your work for every answer.

ECEn 370 Introduction to Probability

6.042/18.062J Mathematics for Computer Science December 14, 2004 Tom Leighton and Eric Lehman. Final Exam

MAT 271E Probability and Statistics

1.1 Review of Probability Theory

MAS108 Probability I

3 Continuous Random Variables

CDA6530: Performance Models of Computers and Networks. Chapter 2: Review of Practical Random

MATH Notebook 5 Fall 2018/2019

Introduction to Stochastic Processes

Twelfth Problem Assignment

PRACTICE PROBLEMS FOR EXAM 2

Principles of Mathematics 12

Name of the Student:

Chapter 2 Random Variables

MATH 360. Probablity Final Examination December 21, 2011 (2:00 pm - 5:00 pm)

Discrete Mathematics and Probability Theory Fall 2013 Vazirani Note 12. Random Variables: Distribution and Expectation

Section 9.1. Expected Values of Sums

Random variables. DS GA 1002 Probability and Statistics for Data Science.

Quiz 1 Date: Monday, October 17, 2016

Transcription:

EECS 126 Probability and Random Processes University of California, Berkeley: Fall 2014 Kannan Ramchandran September 23, 2014 Midterm Exam 1 Last name First name SID Rules. DO NOT open the exam until instructed to do so. Note that the test has 105 points. The maximum possible score is 100. You have 10 minutes to read this exam without writing anything. You have 90 minutes to work on the problems. Box your final answers. Partial credit will not be given to answers that have no proper reasoning. Remember to write your name and SID on the top left corner of every sheet of paper. Do not write on the reverse sides of the pages. All eletronic devices must be turned off. Textbooks, computers, calculators, etc. are prohibited. No form of collaboration between students is allowed. If you are caught cheating, you may fail the course and face disciplinary consequences. You must include explanations to receive credit. Problem Part Max Points Problem Part Max Points 1 (a) 8 2 15 (b) 8 3 15 (c) 8 4 15 (d) 8 5 20 (e) 8 40 Total 105 1

Cheat sheet 1. Discrete Random Variables 1) Geometric with parameter p [0, 1]: P (X = n) = (1 p) n 1 p, n 1 E[X] = 1/p, var(x) = (1 p)p 2 2) Binomial with parameters N and p: P (X = n) = ( N n E[X] = Np, var(x) = Np(1 p) 3) Poission with parameter λ: P (X = n) = λn n! e λ, n 0 E[X] = λ, var(x) = λ ) p n (1 p) N n, n = 0,..., N, where ( N n 2. Continuous Random Variables 1) Uniformly distributed in [a, b], for some a < b: f X (x) = b a 1 1{a x b} E[X] = a+b (b a)2 2, var(x) = 12 2) Exponentially distributed with rate λ > 0: f X (x) = λe λx 1{x 0} E[X] = λ 1, var(x) = λ 2 3) Gaussian, or normal, with mean µ and variance σ 2 : f X (x) = 1 exp { (x µ)2 2πσ 2 2σ } 2 E[X] = µ, var(x) = σ 2 ) = N! (N n)!n! 2

Problem 1. Short questions: 8 points each. (a) Suppose that a well-shuffled 52 -card deck is dealt to 4 different players; that is each player receives 13 cards. Find the probability that each player gets an ace. 3

(b) Suppose that you are applying to graduate school, and you want to improve your GRE writing score. Assume that on a given day, your score is distributed uniformly on the set {4, 5, 6} independent of other days. You plan to take 3 exams and report the highest score: X = max{x 1, X 2, X 3 }, where X i is the score in the i -th exam you take. Calculate the PMF of X. 4

(c) Smart Alec uses a Huffman code to compress i.i.d. (independent and identically distributed) strings of symbols that come from a 5 -ary alphabet (A, B, E, R, S) where the probabilities of occurrence of the symbols are given by (1/4, 1/4, 1/6/1/6, 1/6) respectively. How many bits does his encoder need to encode the string BEARS? 5

(d) Two points A and B are picked randomly on a circle of unit radius, and are connected by drawing line AB. Then, two more points C and D are picked randomly and independently from other points on the circle, and are connected by drawing line CD. What is the probability that AB and CD intersect? 6

(e) There are two coins. The first coin is fair. The second coin is such that P (H) = 3/4 = 1 P (T ). You are given one of the two coins, with equal probabilities between the two coins. You flip the coin four times and three of the four outcomes are H. What is the probability that your coin is the fair one? 7

Problem 2. (15 points) Consider two continuous random variables X and Y that are uniformly distributed with their joint pdf equal to A over the shaded region as shown below. Figure 1: Joint pdf of X and Y (a) What is A? (b) What are E(X) and E(Y )? (c) Are X and Y independent? Are X and Y uncorrelated? Explain. 8

Problem 3. (15 points) Target and Safeway pharmacies have the following two customer serving policies. At Target pharmacy, there is a central queue that customers join. As soon as one cashier is free, the head of the line customer of the queue goes to the cashier for service. At Safeway pharmacy, there is one queue per cashier. Once a customer has picked his/her queue, he/she cannot change the queue. Assume that the service time of each customer, T, is a random variable that is exponentially distributed with mean 1 minute. That is, f T (t) = e t 1{t 0}. Furthermore, different customers have independent service times. Now consider the situation that you just finished your shopping and want to check out. All cashiers are busy but nobody is waiting to be served. (a) Suppose there are 2 cashiers. What is your expected waiting time plus serving time if you shop at Target pharmacy? What is your expected waiting time plus serving time if you shop at Safeway pharmacy? Which queueing strategy do you think is better? 9

(b) Now suppose that there are n cashiers, and again no other customers in the queue. Find your expected waiting and serving time in both pharmacies. 10

Problem 4. (15 points) A circle of unit radius is thrown on an infinite sheet of graph paper that is grid-ruled with a square grid with squares of unit side. (See Figure??.) Assume that the center of the circle is uniformly distributed in the square in which it falls. Find the expected number of vertex points of the grid that fall in the circle. Figure 2: Random circle on a grid-ruled plane. 11

Problem 5. (15 points) Suppose that we have n bins around a circle as shown in Figure??. Consider a wheel of fortune that covers d adjacent bins uniformly at random, independent of all other spinnings. Each time that the wheel is spun, a ball is thrown in each of the d covered bins. Suppose that the wheel is spun m times. Figure 3: Diagram of n bins around of a circle. (a) What is the distribution of the number of balls in a particular bin? What are the mean and variance? 12

(b) Suppose that n = 1000, m = 100 and d = 2. What is a good approximation of the distribution in part (a)? (c) Let E i be the event that bin i is non-empty. Find Pr(E i+1 E i ) for i < n. Are the events E i and E i+1 independent? Justify. 13

(d) Compute the expected number of bins that are empty? (e) Let E be the event that at least one bin is empty. Let m = n ln(n) and d = 2. Find lim n Pr(E). [Hint: You may find the following useful: lim x (1 + 1 x )x = e.] 14

END OF THE EXAM. Please check whether you have written your name and SID on every page. 15