Introduction to Stochastic Processes 1

(STATS217, Winter 2023)
Resources: Our official course text is ``Essentials of Stochastic Processes'' by Rick Durrett, freely available here. You may also find the following additional resources useful. Prerequisites:

Course Policies: A detailed overview of course policies (including grading and assignments) can be found in the course syllabus.

Course description

This course is an introduction to discrete stochastic processes. We will see how to model real-world stochastic processes as simple, structured random systems, and how doing so gives us the power to draw remarkably precise, controlled conclusions about the macroscopic behavior of these chaotic processes. Topics covered include discrete and continuous time Markov Chains, Martingales, Poisson Processes, and some topics in Statistical physics. This is a rigorous, theoretical, proof-based course, but we will not require knowledge of measure theory.

Lectures and Reading
Below is a preliminary schedule (subject to change), including the readings relevant to each lecture.