This Course at MIT pages provide context for how the course materials published on OCW were used at MIT. They are part of the OCW Educator initiative, which seeks to enhance the value of OCW for educators.
This page focuses on the course 15.070J Advanced Stochastic Processes as it was taught by Professor David Gamarnik in Fall 2013.
15.070J is an advanced graduate level lecture-based course devoted to the theory of random processes.
Course Goals for Students
- Gain a deeper understanding of stochastic processes including martingale theory, filtration and stopping theorems, concentration inequalities, large deviations theory, Brownian motion, stochastic integration, Ito calculus, weak convergence and functional limit theorems.
- Explore applications to finance theory, insurance, queueing and inventory models.
Any one of the following courses:
- 6.431 Applied Probability
- 15.085J Fundamentals of Probability
- 18.100 Real Analysis (18.100A, 18.100B, or 18.100C)
- H-Level Graduate Credit
- Operations Research Center (ORC) doctoral degree requirement
Every fall semester
Breakdown by Year
This course is primarily taken by graduate students.
Typical Student Background
A majority (2/3-3/4) of students are coming from an engineering angle (especially electrical engineering and computer science) and operations research, with the remaining from business, economics and mathematics backgrounds.
During an average week, students were expected to spend 12 hours on the course, roughly divided as follows:
- One or two 90-minute class sessions per week
- Mandatory attendance
- 22 class sessions total
Out of Class