STA 3431F: Monte Carlo Methods (Fall 2018)

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, to be updated after each lecture.

Instructor: Professor Jeffrey S. Rosenthal, Department of Statistics, University of Toronto. Sidney Smith Hall, room 5022; phone (416) 978-4594; e-mail j.rosenthal@math.toronto.edu; web http://probability.ca/jeff/

Lectures: Mondays, 10:10 a.m. - 12:00 noon, Sidney Smith Hall (SS) room 1072. First class Sept 10. Last class Dec 3. No class Oct 8 (Thanksgiving). For Reading Week, there is *NO* class on Oct 29, but there *IS* class on Nov 5. 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 probability.ca/sta3431 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 (including "R"). This course is intended primarily for Statistics Department graduate students; all others must request permission from the instructor (by Sept 18 at the latest) to enrol.

Evaluation:
5% Class attendance / punctuality / preparation / attention / participation
20% Homework #1 (assigned by Sept 24, due Oct 15 at 10:10 a.m. sharp)
20% Homework #2 (assigned by Oct 22, due Nov 12 at 10:10 a.m. sharp)
30% Test (Nov 19, in class; 100 minutes)
20% Project (assigned by Oct 30, due Nov 26 at 10:10 a.m. sharp)
5% Presentation (Nov 26 or Dec 3; 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 office hours on Thurs Oct 11 from 1:30-3:00, and on Fri Oct 12 from 11:10-12:00.]

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 probability.ca/sta3431, or permanently at probability.ca/jeff/teaching/1819/sta3431/