Spring 2022

Stochastic Processes

Listed in: Mathematics and Statistics, as MATH-365


Tanya L. Leise (Section 01)


A stochastic process is a collection of random variables used to model the evolution of a system over time. Unlike deterministic systems, stochastic processes involve an element of randomness or uncertainty. Examples include stock market fluctuations, audio signals, EEG recordings, and random movement such as Brownian motion and random walks. Topics will include Markov chains, martingales, Brownian motion, and stochastic integration, including Ito’s formula. Four class hours per week, with weekly in-class computer labs.

Requisite: MATH 360 or consent of the instructor. Limited to 24 students.  Professor Leise. 

Students who enroll in this course will likely encounter and be expected to engage in the following intellectual skills, modes of learning, and assessment: Problem sets, Use of computational software, In-class or take-home exams. Students with documented disabilities who will require accommodations in this course should be in consultation with Accessibility Services and reach out to the faculty member as soon as possible to ensure that accommodations can be made in a timely manner.

If Overenrolled: Preference will be given to math majors and then to juniors and seniors.

Cost: $93 ?

This is preliminary information about books for this course. Please contact your instructor or the Academic Coordinator for the department, before attempting to purchase these books.

ISBN Title Publisher Author(s) Comment Book Store Price
Introduction to Stochastic Processes with R (1st edition) Wiley; 1st edition (March 7, 2016) Robert Dobrow TBD


2023-24: Not offered
Other years: Offered in Spring 2014, Spring 2016, Spring 2018, Spring 2022