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CME 308: Stochastic Methods in Engineering (MATH 228, MS&E 324)

Spring Quarter, 2022

All course materials are distributed through Canvas.

Remark: Students wishing to take the course who find that the enrollment cap for CME 308 has been exceeded should consider registering in either Math 228 or MS&E 324 (which have uncapped enrollments).

Regarding CME 308 vs CME 298, CME 308 covers a broader range of topics, at a deeper mathematical level, than CME 298. CME 298 is more engineering oriented, with fewer math details.

Course Information

Professor Peter W. Glynn
Huang Engineering Center, Room 359A
Mon, Wed, Fri 9:45 AM - 11:15 AM in 370-370
Office Hours
Course Assistant
Yanlin Qu Xuhui Zhang Sirui Lin
Canvas Canvas Canvas
Office Hours
Canvas Canvas Canvas
Canvas Canvas Canvas

Course Description

This graduate level course is intended to give students a broad sense of the different mathematical and computational tools and models available to analyze systems in which uncertainty is present. The key ideas underlying stochastic analysis will be presented and illustrated using various applications chosen from engineering, the physical sciences, and economics. This course is intended both to introduce students to the subject matter at an advanced level and to offer an entry point into the many other high-level stochastics courses that Stanford offers.


Students should have a reasonable background in real variables (e.g. limits, epsilon-delta arguments, etc) and linear algebra (e.g. vector spaces, matrices, eigenvalues, eigenvectors, diagonalization). In addition, I will be assuming that students enter the class with some basic preparation in probability (Knowledge of sample space, events, probability, conditional probability, independence, random variables, jointly distributed rvs, probability mass functions, probability density functions, expectations, the law of large numbers, central limit theorem).

Suggested References

Probability and Random Processes by Geoffrey R. Grimmett & David Stirzaker (Oxford)
A Course in Large Sample Theory by T.S. Ferguson (Springer 1996)
Statistical Inference by George Casella and Roger L. Berger (Duxbury)
Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues by Pierre Bremaud (Springer)
See also Math 136 Lecture Notes by Amir Dembo (for a treatment on probability theory)


There is no required textbook for this class.


There will be five assignments over the course of the quarter, but one assignment can be missed. Collaboration among students is encouraged. You should feel free to discuss problems with your fellow students (please document on each assignment with whom you worked). However, you must write your own solutions, and copying homework from another student (past or present) is forbidden. The Stanford Honor Code will apply to all assignments, both in and out of class.


Midterm exam: TBD.

Final exam: TBD.


The course grade will be 52% homework, 16% midterm and 32% final.


All course materials will be distributed via Canvas.



Last modified Saturday, 26-Mar-2022 23:34:48 PDT