Spring 2016

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 instructor. Limited to 24 students. Spring semester.  Professor TBA.

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


Quantitative Reasoning


2022-23: Not offered
Other years: Offered in Spring 2014, Spring 2016, Spring 2018, Spring 2022