Skip to content

Converted source. index.md — berkeley-stat206b · spring-2026, licensed CC BY 4.0. Converted 2026-09-20 from .md. The same text in markdown, split so that every part has a URL; nothing here is rewritten.

Stat 206b: Advanced Topics in Probablity and Stochastic Processes

Introduction to Integrable Probability (Math 223b/Stat 206b) uses bCourses in Spring 2026.

Course Topics

This course introduces probabilistic models in which large-dimensional stochastic systems can be analyzed exactly using algebraic techniques. Principal examples arise in two-dimensional statistical mechanics, including random tilings, last passage percolation, six-vertex model, interacting particle systems, and random matrices. On the methods side, we will do many computations in the algebra of symmetric polynomials, learn about determinantal point processes, and use of difference operators in the asymptotic analysis.

Prerequisites

Basics of probability theory, linear algebra, and complex analysis. Experience with rigorous mathematical proofs.

This is a graduate class. Undergraduates who would like to take the class, must attend the first lecture. After the lecture, they can contact the instructor for the enrollment code.


Up: contents