15.070J | Fall 2013 | Graduate

Advanced Stochastic Processes

Instructor Insights

Course Overview

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 Outcomes

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.

Curriculum Information


Any one of the following courses:

Requirements Satisfied


Every fall semester

Student Information


About 25 students

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.

How Student Time Was Spent

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

Time divided between:

Course Info

Learning Resource Types
Problem Sets with Solutions
Exams with Solutions
Lecture Notes
Instructor Insights