ECE 581: Random Signals and Noise (FA25)

Course Information

• Lecture: Wilkinson 132 MW 1:25 - 2:40 PM

  • Prof. Henry Pfister

  • Dr. Pfister Office Hour: T 3-4pm and TH 9:30-10:30am

  • Prerequisite(s): Graduate standing or UG probability (STA 130L/240L or Math 230/340 or ECE 380/555 or EGR 238L)

• Syllabus (updated 11/11/25)

• Primary Textbooks

  • Probability and Stochastic Processes (PSP) by Yates and Goodman, 3rd Ed.

    • Use the O'Reilly mobile app with Duke SSO to download e-book

  • Fundamentals of Applied Probability and Random Processes (FAP) by Ibe, 2nd Ed.

• Other Textbooks and References

  • Undergraduate Probability I (UPI) by Chamberland (toc ch1 ch2 ch3 ch4 ch5 ch6 ch7 ch8 ch9 ch10 ch11 ch12)

  • Probability, Random Processes, and Statistical Analysis (PRPSA) by Kobayashi and Turin

  • First Course in Probability (7th) by S. Ross

  • Schaum's Outline of Probability, Random Variables, and Random Processes by Hwei Hsu

Lecture Slides

• Lecture 1 - Introduction

• Lecture 2 - Sequential Experiments

• Lecture 3 - Discrete Random Variables

• Lecture 4 - Continuous Random Variables

• Lectures 5-7 - Multiple Random Variables

• Lecture 8

Handouts

• Geometric sums and the variance of a geometric random variable

• The cdf of a Poisson and the incomplete gamma function

• Functions of random variables

• Inner product spaces of random variables

• Convergence for Sequences of Random Variables

• Finite-State Markov Chains

• Descriptive and Inferential Statistics

• Gaussian processes

• Random Processes

Problems and Assignments

• Cognitive Strategies for Learning Probability
  

• In Class Problems 1
  

• Homework 1
  

• In Class Problems 2
  

• Homework 2
  

• In Class Problems 3
  

• Homework 3 (soln)
  

• In Class Problems 4
  

• Homework 4
  

• In Class Problems 5
  

• Homework 5
  

• Computer exercise for functions of random variables
  

• Homework 6
  

• Computer exercise for least squares estimation
  

• Homework 7
  

• Computer exercise for Markov chains
  

Links

• Detailed introduction to spectral theory of Markov chains

Web Resources

• Wikipedia

  • History of Probability, Interpretations, Probability Space

  • Random Variable, Binomial Distribution, Normal Distribution

  • Bayes’ Theorem, Maximum Likelihood Estimation

  • Stochastic Process, Markov Chain, Gaussian Process

• Historical Figures

  • Famous Figures in Probability and Statistics