Course Description
This is a graduatelevel 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, meansquare 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 DiscreteTime Linear Systems

Part 2: WideSense Stationary (WSS) Random Sequences


Random Processes

Part 1: Introduction and Study of Common Random Processes (10 Videos, ~87 minutes)

Part 2: Markov Random Processes and ContinuousTime Linear Systems

Part 3: Classification and WSS Random Processes

Part 4: Additional Classification


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

Random Vectors (5 videos, ~36 minutes)
Practice Problems
File Downloads