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.
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.
|Introduction to Stochastic Processes with R (1st edition)||Wiley; 1st edition (March 7, 2016)||Robert Dobrow||TBD|