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Course description: This course covers probability, random variables, discrete and continuous distributions, transformation of random variables, expectation, generating functions, statistical inference, hypothesis testing, estimation, random processes, stationarity, and applications.

Prerequisite: Credit or concurrent registration in ECE 310.

Instructor: Natasha Devroye Science and Engineering Offices (SEO) room 1039 Website Office hours: Monday after class, 1:50pm - 3pm and TBD

Teaching Assistant: Diana Maamari Office hours: TBD

Lecture: MWF 1-1:50pm, Douglas Hall 210


  • Probability
  • Random Variables
  • Discrete and Continuous Distributions
  • Joint and Conditional Densities
  • Transformation of Random Variables
  • Expected Values and Moment Generating Functions
  • Hypothesis Testing
  • Estimation in Communications
  • Gaussian Random Variables
  • Random Processes, Gaussian and Poisson Arrival Process
  • Autocorrelation and Stationarity
  • Applications
ECE-related application areas that may be covered:

  • Amplitude limiting (mixed discrete and continuous random variables)
  • Amplitude quantization, signal-to-noise power ratio
  • Basic information theory, entropy of a discrete random variable
  • Huffman coding, efficiency of a binary code
  • Sample mean, biased and unbiased estimators
  • Bit-rate-error analysis of a noisy communication channel
  • Correlation coefficient, linear estimation
  • Estimating PDF: histogram method, moment generating function method
  • Estimating PSD: autocorrelation method
  • Linear filtering stationary random processes
Simulations: We will use the MATLABŪ programming language throughout the course to confirm predictions from probability theory. This will be done by performing experiments using simulated random variables and by plotting various statistical waveforms. MATLABŪ is available for students use on both ACCC and ECE computer networks; a personal student version may also be purchased from Mathworks, Inc. (see

Grading: (grades will be posted on Blackboard)

  • Class participation 3%
  • Homework and Matlab (weekly) 17%
  • Random in-class quizzes (will give 5 over the semester) 10%
  • Midterm 1 20% (may bring one 8.5x11 inch double-sided cheat sheet) 02.06.2012
  • Midterm 2 20% (may bring two 8.5x11 inch double-sided cheat sheets) 03.16.2012
  • Final 30% (may bring three 8.5x11 inch double-sided cheat sheets) This grade may be used to replace one midterm grade if it's better.
Policy: Late homeworks will under no circumstances be accepted. There will be no make-up midterms or finals. Solution manuals are known to be available for this course; it is strongly encouraged that you solve problems and learn think independently, NOT by looking at the solutions. This will decrease your understanding and ultimately haunt you in the midterms and finals.

Textbook chapters (subject to change): Chapters 1-5
Chapter 6 (§6.1-6.7)
Chapter 7
Chapter 9 (§9.1-9.2)
Chapter 10 (§10.1-10.3,10.8-10.11)
Chapter 11 (§11.1, 11.5, 11.7, 11.8)

Textbook: “Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers” by Roy D. Yates and David J. Goodman. John Wiley & Sons; 2nd edition, 2004

. Textbook image

ISBN-10: 0471272140, ISBN-13: 9780471272144

WARNING: It is urgent that you read the "A Message to Students from the Authors" on pages xi-xii of the textbook.

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Topic revision: r5 - 2012-01-09 - 15:14:21 - Main.devroye
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