6.262 | Spring 2011 | Graduate

Discrete Stochastic Processes

Course Description

Discrete stochastic processes are essentially probabilistic systems that evolve in time via random changes occurring at discrete fixed or random intervals. This course aims to help students acquire both the mathematical principles and the intuition necessary to create, analyze, and understand insightful models for a …
Discrete stochastic processes are essentially probabilistic systems that evolve in time via random changes occurring at discrete fixed or random intervals. This course aims to help students acquire both the mathematical principles and the intuition necessary to create, analyze, and understand insightful models for a broad range of these processes. The range of areas for which discrete stochastic-process models are useful is constantly expanding, and includes many applications in engineering, physics, biology, operations research and finance.
Learning Resource Types
Problem Sets with Solutions
Exams with Solutions
Online Textbook
Lecture Videos
Diagram of two stable M/M/1 queues.
Tandem queues: A stable M/M/1 queue has a Poisson output at the input rate. The next queue also has a Poisson output at that rate. (Image by MIT OpenCourseWare, adapted from Prof. Robert Gallager’s course notes.)