2005 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 2005 Fall 216 Section Number 01 Primary Instructor Huber,Mark Prerequisites Prerequisite: Mathematics 135 or equivalent. Course Homepage courses.duke.edu
Prerequisites
Math 104 and Math 135 or the 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 the most important types of stochastic processes: Markov chains, martingales, and Brownian motion. The goal is develop a thorough practical and theoretical understanding of these processes for use in fields including statistics, randomized algorithms, and finance. Roughly the breakdown for this course is 2/3 theory, and 1/3 applications.
Textbooks
Lawler, Introduction to Stochatic Processes
Assignments
Assignments will be weekly. Usually there will be 4 regular problems (usually multipart) dealing with basic understanding and theory and a computer implementation problem dealing with applications.
Exams
Two inclass midterms, one final. The midterms will test knowledge of terms, definitions, and theorems covered in the course. The final will be comprehensive.
Term Papers
none
Grade to be based on
Homework (written problems) 20%
Homework (computer problems) 15%
Midterm 1 20%
Midterm 2 20%
Final 25%
Additional Information
The computer work can be done in the language of the student's choice, in the course I will teach how to implement processes in MATLAB for those with no prior programming experience.
