STA 447/2006S: Stochastic Processes (Winter 2018)

STA 447/2006S is a course about random (stochastic) processes, designed for graduate and senior undergraduate students in statistics and related disciplines. [See also the evolving lecture notes, 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; web

Lectures: Thursdays, 6:10 - 9:00 p.m., in room 2102 of Sidney Smith Hall (building "SS" on campus map). First class Jan 4. Last class March 29. No class Feb 22 (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.

Prerequisite: STA347. NOTE: This prerequisite will be strictly enforced for undergraduate students: undergraduate students without STA347 will not be permitted to remain in STA447 except in very special circumstances. (It does not suffice to simply have taken some other advanced statistics courses.) For graduate students, it suffices to have taken a course equivalent to STA347 at another university; if you are unsure about the equivalence then please ask me.

Evaluation (tentative, to be confirmed later):
10% Homework #1 (assigned by Jan 11, due Jan 25 at 6:10 pm sharp);
25% Midterm test (on Thurs Feb 8, during class time);
10% Homework #2 (assigned by Feb 15, due Mar 8 at 6:10 pm sharp);
10% Homework #3 (assigned by Mar 15, due Mar 29 at 6:10 pm sharp);
45% Final Exam (some time during April 9-30 exam period, to be announced).

Instructor Office Hours: You are welcome to talk to the instructor after class, or any time you find him in his office (SS 5022), or you can e-mail him to arrange another time to meet.

TA Office Hours: To be announced.

Tentative list of topics to be covered: Markov chains in discrete and continuous time, martingales, Poisson processes, renewal theory, and Brownian motion, with applications (as time permits) to Monte Carlo algorithms, random walks on graphs, branching processes, option pricing, queueing networks, and more.

Readings: There is no required textbook. The instructor will post his point-form lecture notes on this course web page. In addition, the following books (among others) may be useful for further reading:

Lateness policy: Homeworks are due at 6:10pm sharp. Lateness penalties are: 1-5 mins = 1 point; 6-10 mins = 2 points; 11-30 mins = 5% of total points; 31 mins - 24 hours = 10% of total points; longer = (10% of total points) x (number of days late, rounded UP).

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 homework or test was first available. Warning: your mark may end up going down rather than up. (Note: for final exams, a different Faculty-wide process is followed.)

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