MATH 136 – Stochastic Processes | 2022-2023

This is the webpage for the course MATH 136 – Stochastic Processes.


Lecture times: Tuesday+Thursday 1:30 PM – 2:50 PM (Room: 260-113).

Instructor: Jacopo Borga ( Office hours: Thursday 11:00-12:00 AM (Room: 382-Q2).

Assistant: Christian Serio ( Office hours: Wednesday 1:00-3:00 PM (Room: 381-D).

Assistant: Zhongren Chen ( Office hours: Monday 4:30-6:30 PM (Room: TBA).

Description: This course prepares students to a rigorous study of Stochastic Differential Equations, as done in Math236. Towards this goal, we cover – at a very fast pace – elements from the material of the (Ph.D. level) Stat310/Math230 sequence, emphasizing the applications to stochastic processes, instead of detailing proofs of theorems. A critical component of Math136/Stat219 is the use of measure theory.

The Stat217-218 sequence is an extension of undergraduate probability (e.g. Stat116), which covers many of the same ideas and concepts as Math136/Stat219 but from a different perspective (specifically, without measure theory). Thus, it is possible, and in fact recommended to take both Stat217-218 and Math136/Stat219 for credit. However, be aware that Stat217-218 can not replace Math136/Stat219 as preparation for a study of Stochastic Differential Equations (i.e. for Math236).

Main topics of Math136/Stat219 include: introduction to measurable, Lp and Hilbert spaces, random variables, expectation, conditional expectation, uniform integrability, modes of convergence, stationarity and sample path continuity of stochastic processes, examples such as Markov chains, Branching, Gaussian and Poisson Processes, Martingales and basic properties of Brownian motion.

Prerequisites: Students should be comfortable with probability at the level of Stat116/Math151 (summary of material) and with real analysis at the level of Math115. Past exposure to stochastic processes is highly recommended.

Program: We will mainly follow this lecture notes written by Prof. Amir Dembo. There is no required textbook. Here is a tentative program for the course (the numbers below refer to the Sections in the lecture notes):

Week 1 (09/26): Tu(1.1/1.2.1/1.2.2) Th(1.2.3)

Week 2 (10/03): Tu(1.3) Th(1.4)

Week 3 (10/10): Tu(2.1) Th(2.3/2.4)

Week 4 (10/17)Tu(3.1/3.2.1/3.2.2) Th(3.2.3/3.3)

Week 5 (10/24): Tu(5.1) Th(4.1.1/4.1.3)

Week 6 (10/31): Tu(Midterm exam) Th(4.2/4.3.1)

Week 7 (11/07)Tu(Democracy Day: day of civic service – no class) Th(4.3.1/4.3.2)

Week 8 (11/14): Tu(4.4.1) Th(5.2/5.3) 

*November 21-25 (Mon-Fri) Thanksgiving Recess (no classes)

Week 9 (11/28): Tu(4.4.2/4.5/4.6) Th(6.1)

Week 10 (12/05): Tu(6.1/6.2) Th(exercise session)

Exercises: A new homework (with problems/exercises from the lecture notes) will be posted at the start of every week (or earlier), and should be submitted on Gradescope by 10PM the following Tuesday. I will set up Gradescope to actually accept submissions until a day later, as a grace period in case of last-minute technical difficulties. Any other delay will be not accepted (i.e., no grading for late submissions). Also, your lowest homework score will be dropped from consideration.

You are permitted (and encouraged!) to discuss the problems with other students, but you must write up the solutions yourself. Please work out problems neatly — do not hand in your scratch work.

Midterm exam: Tuesday, November 1st at 1:30-3:00 PM (on the material covered on Weeks 1-2-3-4). You are allowed to bring with you only a printed copy of the lecture notes of the course (with your annotations, but without additional material such as solutions to exercises).

Final exam: Tuesday, December 13th at 3:30-6:30 PM (Room: ?). You are allowed to bring with you only a printed copy of the lecture notes of the course (with your annotations, but without additional material such as solutions to exercises).


Grading: Judgement based on Midterm exam mark (30%), Final exam mark (40%) and on consistent Homework (30%).

Additional info: Consistent with university policy, face coverings are required in classrooms until further notice. Face coverings may, however, be removed briefly while speaking. As Stanford strongly recommends masking indoors, face coverings should also be worn when attending office hours. Eating is not permitted in classrooms. Students should attend the in-person lecture and discussion section to which they are officially assigned. This ensures that classrooms do not exceed official room capacity, and supports prompt notification should a specific section need to move online at short notice. Course messages are sent out via Canvas, please ensure that your Canvas notifications are on so that you receive any announcements promptly. As standard practice, lectures and discussion sections in Mathematics courses are taught in-person. As such, Zoom links will not be provided. Additionally, in-person lectures and discussion sections will not be recorded. Students who miss class due to illness (including COVID-19) should make arrangements to obtain lecture notes from other students in the class. As standard practice, there are no make-up exams or remote exams. If you will miss an exam due to illness, please reach out to your instructor for more information.