STA 3431F: Monte Carlo Methods (Fall 2017)

This course will explore Monte Carlo computer algorithms, which use randomness to perform difficult high-dimensional computations. Different types of algorithms, theoretical issues, and practical applications will all be considered. Particular emphasis will be placed on Markov chain Monte Carlo (MCMC) methods. The course will involve a combination of methodological investigations, mathematical analysis, and computer programming. [See also the evolving lecture notes and supplementary files.]

Instructor: Professor Jeffrey S. Rosenthal, Department of Statistics, University of Toronto. Sidney Smith Hall, room 5022; phone (416) 978-4594; e-mail; web

Lectures: Mondays, 10:10 a.m. - 12:00 noon, in room 2105 of Sidney Smith Hall (building "SS" on campus map). First class Sept 11. Last class Dec 4. No class Oct 9 (Thanksgiving) nor Nov 6 (Reading Week). During lectures, please put away your laptops and cell phones (unless you are using them specifically for a class-related purpose with prior permission), and pay attention to the material being presented.

Course Web Page: Visit for course information and announcements.

Prerequisites: Knowledge of statistical inference and probability theory and basic Markov chains at the advanced undergraduate level, and familiarity with basic computer programming techniques. This course is intended primarily for Statistics Department graduate students; all others must receive permission from the instructor to enrol.

20% HW#1 (assigned by Sept 25, due Oct 16 at 10:10 sharp)
20% HW#2 (assigned by Oct 23, due Nov 13 at 10:10 sharp)
30% Test (Nov 20; 100 minutes)
25% Project (assigned by Oct 30, due Nov 27 at 10:10 sharp)
5% Presentation (Nov 27 or Dec 4; 5 min; in inverse alphabetical order by surname)

Instructor Office Hours: You are welcome to talk to the instructor after class, or any time you find him in his office, or you can e-mail him to arrange another time to meet. Special pre-test office hours: Friday, Nov 17, 12:30, in SS 5022.

Lateness policy: Homeworks are due sharply at the appointed time, and will receive significant penalties if they are late.

Regrading policy: Regrading requests should only be made for genuine grading errors, and should be initiated by writing or typing a complete explanation of your concern (together with your full name, student number, e-mail address, and telephone number) on a separate piece of paper, and giving this together with your original unaltered homework/test paper to the instructor within one week of when the graded item was first available. Warning: your mark may end up going down rather than up.

This document is available at, or permanently at