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 methods. The course will involve a combination of methodological considerations, mathematical analysis, and computer programming.
Prerequisite: This course is designed for graduate students in the Department of Statistics. All others should e-mail the instructor for permission to enrol, which will only be granted if you have significant background in statistics and probability. Some familiarity with basic computer programming (e.g. if statements, for loops, etc.) is also required.
Time: Mondays, 2-4. (First class January 5, last class April 6, no class Febrary 16 [Reading Week].)
Place: Bahen Centre, room 2145. (Building "BA" on campus map.)
Course Web Page: http://probability.ca/sta3431
References: There is no required textbook, but much of the material to be covered is discussed in various sources, including:
Instructor: Professor Jeffrey S. Rosenthal, Department of Statistics, University of Toronto. Sidney Smith Hall, room 6024; 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 including C and Java, and some other languages will be accepted as substitutes on the homework assignments.
|Item:||Worth:||Assigned by:||Due (in class, by 2:10 sharp):|
|HW#1||10%||Jan 19||Feb 2|
|HW#2||12%||Feb 2||Mar 2|
|HW#3||13%||Mar 2||Mar 23|
|In-Class Test||30%||N/A||Mar 30 (90 mins)|
|Final Project||35%||Jan 19||April 6|