STA 3431F: Monte Carlo Methods (Fall 2021; online synchronous)

See the evolving lecture notes and supplementary files, to be updated after each class (and you should review/try them before the next class).

Course Web Page: http://probability.ca/sta3431

Prerequisites: Knowledge of statistical inference and probability theory and basic Markov chain theory at the advanced undergraduate level (e.g. skim this book especially the Appendix), and familiarity with basic computer programming techniques (including "R", which you should review on your own if you have not previously used; see e.g. this page).

Instructor: Professor Jeffrey S. Rosenthal, Department of Statistics, University of Toronto. Email j.rosenthal@math.toronto.edu; web http://probability.ca/jeff/

Who May Take This Course? This course is intended primarily for Statistics Department graduate students. Graduate students from other UofT departments must request permission from the instructor (by Sept 9 at the latest) to enrol; email me about your course interest and your program and prerequisites including all your university transcripts; then if I agree, then you are responsible for making up any missing background knowledge, and also for sorting out any necessary forms/paperwork (which I will sign by email where required). Unfortunately, undergraduate/auditing students may not normally take this class, though you are still welcome to work through the course materials on this web site on your own.

Lectures: Mondays, 10:10 a.m. - 12:00 noon (Toronto time), online synchronous (on Zoom). First class Sept 13. Last class Dec 6. No class Oct 11 (Thanksgiving) nor Nov 8 (Reading Week). Lectures will be interactive; please put away your cell phones and pay close attention to the material being presented.

Zoom information: All lectures will given live (synchronous) over Zoom. They will not be recorded. Here is some information about using Zoom in this class:

1. The Zoom link will be emailed to all enrolled students before the first class. That same link will work for all the classes (and also for any office hours, etc). Please save and bookmark it. However, please do not post it publicly, to avoid zoom-bombers. (If you have not received the Zoom link by Sept 12, then please email the instructor to receive it.)
2. You should connect on Zoom before the class start time of 10:10, hopefully between 10:00 and 10:05.
3. Please connect from a quiet room, where you can be alone without any distractions.
4. Please use a laptop or desktop computer (not an iPad or cell phone), and the actual Zoom app (not the web browser version), for best funcationality.
5. Please leave your computer's camera on, so we can all see each other.
6. Optionally, you may wish to mute your microphone when you are not speaking, to prevent any background noises from disrupting the class. (Then unmute whenever you wish to speak.)
7. If necessary, use "Rename" on Zoom to make it display your usual first and last name (but not your student number).
8. You should pay close attention during class, and refrain from sending or reading any private messages, emails, Facebook posts, etc.
9. During class, the instructor will use "share screen" to display his lecture notes and computer simulations. You should be able to see both the instructor and the screen he is sharing. All of the notes and computer programs will later be posted on the web.

Instructor Office Hours: You are welcome to talk to the instructor on Zoom after class, or you can email your questions to him, or you can email him to arrange a time to talk on Zoom. Special office hours (on the usual class Zoom channel): Tuesday, Oct 5, 4:30-5:30 pm. Tuesday, Oct 26, 4:30-5:30 pm. Tuesday, Nov 23, 4:30-5:30 pm.

Discussion Pages: I created a general STA3431 Discussion Page on the course's quercus page, where students can post comments and questions about the course. Feel free to post course-related messages there any time you want to. I may or may not read your posts myself, but other students can answer them whenever they wish. And, some students have created a STA 3431 slack channel, which will be announced in class. Also, feel free to create a STA3431 discord page, or form a study group, or join a drop-in study space.

Evaluation:
• 24% Class Participation (your attendance / punctuality / preparation / attention / questions / responses during lectures);
• 18% Homework #1 (assigned by Sept 20, due Oct 8)
• 18% Homework #2 (assigned by Oct 12, due Oct 29)
• 18% Homework #3 (assigned by Nov 1, due Nov 26)
• 4% Presentation (in class on Nov 29 or Dec 6)
• 18% Project (assigned by Oct 25, due Dec 17)

Regrading policy: Regrading requests should only be made for genuine grading errors, and should be initiated by emailing the instructor a complete explanation of your concern (together with your full name, student number, e-mail address, and telephone number), within one week of when the graded item was first available. Warning: your mark may end up going down rather than up. For more details click here.

Supplementary Reading: There is no required textbook (aside from the lecture notes), but much of the material to be covered is discussed in various sources. The book closest to this course is probably:
*** C.P. Robert and G. Casella (2005), Monte Carlo Statistical Methods. [library / amazon]
Certain on-line materials might also be useful, including:
*** Chapters 7,8,9 of Robert Gray's on-line notes.
*** Chapters 4,5,6 of Galin Jones' on-line notes.
*** Chapter 2 of Gareth Roberts' on-line notes.
In addition, the following book chapters might be helpful:
*** Chapters 2,5,6 of J.S. Liu (2001), Monte Carlo Strategies in Scientific Computing. [library / online / amazon]
*** Chapters 6,7,8 of G.H. Givens and J.A. Hoeting (2005), Computational Statistics. [library / amazon ]
*** Chapters 10,11,12,13 of J.F. Monahan (2001), Numerical Methods of Statistics. [library / amazon]

Challenges? If you encounter challenges during your studies, then please visit Academic Success or the Health and Wellness Centre or Graduate Wellness Services or the SGS Wellness Portal or Navi for assistance and support.