STAT 31512: Analysis of Sampling Algorithms

Instructor: Frederic Koehler

Robot shuffling cards

Class Schedule & Location

Monday and Wednesday, 3:00 PM - 4:20 PM

Location: Ryerson 176

Course Description

This is a graduate course on the mathematical analysis of algorithms for sampling from high-dimensional probability distributions. The course mostly focuses on the analysis of Markov-Chain Monte Carlo (MCMC) algorithms via functional inequalities. It aims to equip students with a deep understanding of the mathematical principles underlying these algorithms with a view towards current research questions.

Lecture Schedule

Scribe Instructions

Every student enrolled for credit should scribe a class. Scribe notes should aim to be high quality, mathematically correct, and intelligible to someone who missed the class. They are due within 9 days of when the class occurred. I might ask for edits to the scribe notes if I observe areas for improvement. They will be posted on the course webpage.

Scribing template: template.tex Please send tex and pdf when completed with scribing.

Some useful references and notes

Other Logistics

There will be a course project with a written report for all students enrolled for a grade. You may either write a survey of at least 2 papers, or do original research. The main goal is to make a novel intellectual contribution, so do not just reproduce the contents of an existing paper. For example, even if you are doing a survey try to identify important open problems and whether the techniques in the paper have limitations, try to simplify the proofs, etc. Research that seeks to find new domains of application and connect with theory is also valued. Please send a half-page project proposal by end of Friday, April 5. Some papers of interest: Google doc

If you are auditing the class, please email me to ensure you receive relevant announcements.