STAC63: Probability Models

Contact Information


Prof Daniel M. Roy

Email: (please include “STAC63” in your email subject line)

Office hours: Mondays 3--4pm in IC 462, or by appointment. Changes will be announced.


Tommy Guo

Email: (please include “STAC63” in your email subject line) 

See section on Marking below.


Mondays 7--10pm in IC 120.

There are twelve lectures: The first lecture is September 14. (Recall that September 7 is Labour Day.) There is no lecture on October 12 due to Reading Week. The last MONDAY lecture is November 30, but the LAST LECTURE is Thursday, December 3 (making up for September 7.)


This course continues the development of probability theory begun in STAB52. Probability models covered include branching processes, birth and death processes, renewal processes, Poisson processes, queuing theory, random walks, and Brownian motion.



Blackboard and Course Webpage

I will use a mixture of the course website ( and Blackboard to post material and announcements.


Each student’s grade in the course will be based on:

Structure of the Course

The following is a tentative outline of the material we will cover:


Students with diverse learning styles and needs are welcome in this course. Please feel free to approach me or Accessibility Services so we can assist you in achieving academic success in this course. If you have not registered with the Accessibility Services and have a disability, please visit the Accessibility Services website at for information on how to register.

Advanced notice, especially for exams, is always welcome because this allows me to prepare better. Feel free to approach me in person either before or after class, or at my office. In the latter case, please send me an email ahead of time to make sure I’m available.

Policy on collaboration

Assignments are to be done by each student individually. You may discuss assignments in general terms with other students, but the work you hand in should be your own. In particular, you should not leave any discussion with someone else with any written notes (either paper or electronic). You may not use any resources/aids other than the book and Wikipedia. If you are not certain whether a resource is allowed, email the instructor.

Class participation

In order to obtain full marks for class participation, students should remain attentive, ask for clarification when necessary, offer answers to questions posed by the instructor during class, and present problem solutions on the board during problem-solving sessions. Students can also participate by sharing their (clearly handwritten or typeset) notes with their classmates (via  Blackboard). When programming assignments come with supporting code in one language, say R, class participation credit will be available to anyone who translates the supporting code (but not the answers, of course!), into another language, such as MATLAB or Python.

Policy on Late Work

Assignments are due in class by 7:08pm on the date marked, unless stated otherwise on the assignment. Late assignments will not be accepted. In the case that an official Student Medical Certificate is presented in lieu of an assignment, the missed assignment will not be factored into the student’s grade.


As a general rule, small matters of marking on quizzes and assignments (apparent errors, questions about evaluation criteria, etc.) should be taken first to the marker (via email, listed above). More significant issues, or unresolved matters on assignments, are appropriate to take to the professor. Matters of marking on exams should be taken to the professor.

Required Textbook and other Resources

The required textbook is

  Sheldon M. Ross (2011), Introduction to Probability Models, 11th edition, Academic Press.

I have requested that a copy be put on 3-hour reserve in the library and that the bookstore order copies for purchase.  The book is also available in digital PDF format via the library.

Other Resources

There are many other excellent resources for learning about probability models. The following list contains just a few, some of which are freely available:


Programming assignments can be completed in any language you like, although the assignment handouts and supporting files will generally only be provided in one language (usually R, MATLAB, or Python). To be an effective statistician/data scientist, you must know how to program and manipulate data! Being able to work efficiently with all of the key data science languages is necessary in industry and in applied graduate work.

Language Resources

The R language ( is free software. All public lab machines have R installed. (See for an introduction. There are many other resources online.)  If you have access to a machine where you can install software, you might consider R Studio (, which is an integrated development environment (IDE) that provides many useful features.

The MATLAB language is proprietary but used throughout machine learning. MATLAB 2013 is available in the public Window labs, although you will need a version that has the Statistics toolkit installed to run many packages. Other core machine learning languages include Python and C/C++. Some up and coming languages that have gained a lot of interest within machine learning include Scala and Julia.