Course Description


This is a graduate-level course that teaches basics of random process theory with applications to communication theory and systems.  Important topics include analysis of common random processes (e.g. Poisson process, White Noise, Wiener Process, etc.), random sequences, random processes in linear systems, Markov Chains, mean-square calculus.


The textbook used for the course is, "Probability, Statistics, and Random Processes for Engineers+, 4th Edition, by H. Stark and J. W. Woods.


The video lectures listed below provide a full outline of the course, but only portions of the lectures have been recorded so far.  I try to add a few more recorded lectures each time I teach the course.

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Video Lectures


  • Probability and Random Variable Review

  • Random Vectors

    • Part 1: Basic Definitions and Transformations

    • Part 2: Covariance Matrices and Gaussian Random Variables

  • Random Sequences

    • Part 1: Basic Definitions and Discrete-Time Linear Systems

    • Part 2: Wide-Sense Stationary (WSS) Random Sequences

  • Random Processes

  • Mean Square (MS) Calculus

    • Part 1: Introduction, MS Derivative, and MS Integral

    • Part 2: Ergodicity and the KL Expansion

    • Part 3: Bandpass Random Processes

Example Problems

Practice Problems


File Downloads

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© 2020 by Adam Panagos