EE178: Probabilistic Systems Analysis

Ayfer Özgür, Stanford University, Spring 2018


Course Contents

Introduction to probability and statistics and their role in modeling and analyzing real world phenomena.

  • Basic concepts from probability and statistics: Events, sample space, and probability. Discrete random variables, probability mass functions, independence and conditional probability, expectation and conditional expectation. Continuous random variables, probability density functions, independence and expectation, derived densities. Transforms, moments, sums of independent random variables. Simple random processes. Limit theorems. Introduction to statistics, significance, estimation and detection.

  • Applications chosen from data storage, ranking of webpages, network multiplexing, digital communication, positioning, speech recognition and computational biology.

The course involves both theoretical and computational components: probabilistic concepts are taught through many non-trivial examples, engineering applications and Python programming assignments.


Some mathematical maturity and exposure to programming. Please contact the instructor if in doubt.