This course will explore Monte Carlo algorithms, which use randomness to perform difficult high-dimensional computations. Particular emphasis will be placed on Markov chain Monte Carlo (MCMC) methods. The course will involve a combination of methodological considerations, mathematical analysis, and computer programming.
Prerequisite: Knowledge of statistical inference and probability theory at the advanced undergraduate level, and familiarity with basic computer programming techniques.
Time: Mondays, 2-4. (First class January 4, last class March 30, no class Febrary 15 [Reading Week].)
Place: Lash Miller Chemical Labs (LM), room 123.
Course Web Page: http://probability.ca/sta3431
Instructor:
Professor Jeffrey S. Rosenthal,
Department of Statistics, University of Toronto.
Sidney Smith Hall, room 5016B; phone (416) 978-4594;
http://probability.ca/jeff/;
Computing: Students will be required to write computer programs for this course. The default computer language (required for the in-class test) will be "R", which is freely available and is designed for statistical computation (more information is available here). However, the instructor may also present some examples in other computer languages (e.g. C, Java), and some other languages may (with prior permission) be accepted on the homework assignments.
Evaluation: Homeworks 50% (HW#1 assigned Jan 18, due Feb 8; HW#2 assigned Mar 1, due Mar 22); Project 50% (due Mar 29).
References: There is no required textbook, but much of the material to be covered is discussed in various sources (plus the instructor will provide rough lecture notes). The book closest to this course is probably:
Certain on-line materials might also be useful, including: