Spring 2014

Stochastic Processes

Listed in: Mathematics and Statistics, as MATH-365

Faculty

Tanya L. Leise (Section 01)

Description

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 Leise.

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

Keywords

Quantitative Reasoning

Offerings

2013-14: Offered in Spring 2014
 

Taking Notes