2009 Fall MATH 216-01
Bulletin Course Description An introduction to stochastic processes without measure theory. Topics selected from: Markov chains in discrete and continuous time, queuing theory, branching processes, martingales, Brownian motion, stochastic calculus. Instructor: Staff
(Instructor named in bulletin description above may not be current. For current instructor, see listing below.)
Title APPLIED STOCHASTIC PROC Department MATH Course Number 2009 Fall 216 Section Number 01 Primary Instructor Mattingly, Jonathan Prerequisites Prerequisite: Mathematics 135 or equivalent.
Synopsis of course content
Introduction to stochastic processes including theoretical results and how to implement these processes on a computer. This course is built around fundamental stochastic processes: Markov chains, martingales, and Brownian motion.
Textbooks
Introduction to Stochastic Processes, by Greg Lawler, 2nd edition
Assignments
Problem sets will be assigned every week, or every other week. Usually there will be 4 or 5 regular problems dealing with basic understanding and theory and a computer implementation problem dealing with applications.
Exams
One in-class midterm, one final. The midterm will test knowledge of terms, definitions, and theorems covered in the course, as well as the ability to apply the concepts to simple examples. The final will be comprehensive.
Term Papers
none
Grade to be based on
Homework, mid-term and the final exam.
Additional Information
The computer work can be done in the language of the student's choice, although most of the examples provided in class will be in MATLAB.
