STA 4502S: Topics in Stochastic Processes (Winter 2018)

This course will focus on convergence rates and other mathematical properties of Markov chains on both discrete and general state spaces. Specific methods to be covered will include coupling, minorization conditions, spectral analysis, and more. Applications will be made to card shuffling and to MCMC algorithms.

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

Lectures: Wednesdays, 11:10 a.m. - 1:00 p.m., in room 2120 of Sidney Smith Hall (building "SS" on campus map). First class Feb 28. Last class April 4.

Note: This is a six-week class, and only counts for 0.25 course credit. The add/drop date is the day of the second lecture, i.e. March 7.

Course Web Page: Visit for course information and announcements.

Prerequisites: Graduate-level probability theory with measure theory at the level of STA2111, and stochastic processes at the level of STA447/2006 (may be concurrent). Some linear algebra and group theory will also be used. This course is intended primarily for graduate students in statistics; all others must obtain the permission of the instructor before enroling.

Evaluation: To be announced. Will include some combination of homework assignments, an in-class test, class participation, a project, and/or an oral presentation.

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.

Readings: There is no required textbook. For a first idea of the content of the course, see this paper (discrete case) and this paper and this paper and Section 2 of this paper (general case).

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