The Course Outline contains information about text, topics, grading.
Office hours with G. Slade in MATX 1211: Monday 16:00-16:50, Wednesday 13:00-13:50, Friday 10:00-10:50.
Office hours with Thomas Hughes in MATH 204: Thursday 12:30-13:50.
There are TAs available in the Mathematics Learning Centre whenever the MLC is open. The schedule showing when TAs knowledgeable about probability are available (e.g., Qingsan and Jieliang) is posted on the MLC website.
The last day of regular office hours is Thursday April 6.
There will be an additional office hour with G. Slade on Tuesday April 11, 10:00-11:00 in MATX 1102.
Piazza: you can sign up for MATH 303 with Connect
|Assignment 1 (out Jan 6, due Jan 13)||Solutions|
|Assignment 2 (out Jan 13, due Jan 20)||Solutions|
|Assignment 3 (out Jan 20, due Jan 27)||Solutions|
|Assignment 4 (out Jan 27, due Feb 3)||Solutions|
|Test 1 (Feb 8)||Solutions|
|Assignment 5 (out Feb 10, due Feb 17)||Solutions|
|Assignment 6 (out Feb 17, due Mar 3)||Solutions|
|Assignment 7 (out Mar 3, due Mar 10)||Solutions|
|Assignment 8 (out Mar 10, due Mar 17)||Solutions|
|Test 2 (Mar 22)||Solutions|
|Assignment 9 (out Mar 24, due Mar 31)||Solutions|
Readings: The following is a list of the text sections that are most relevant to the course, in the order discussed in class.
Page limits are inclusive.
Topics where the lectures go beyond the text are marked with an asterisk.
For the 11th edition:
4.1; 4.2 to p. 189; 4.5.1; 4.3 to p. 201; random walk*; 4.4 to p. 207; 4.4.1; 4.8 to p. 242; 4.7 and branching process*; Markov Chain Monte Carlo*; 5.2 to p. 287; 5.3 to p. 304; 5.3.5 Theorem 5.2, Example 5.20, Proposition 5.4; 6.2-6.6; optional: 8.1-8.3.
For the 10th edition:
4.1; 4.2 to p. 197; 4.5.1; 4.3 to p. 211; random walk*; 4.4 to p. 217; 4.4.1; 4.8 to p. 255; 4.7 and branching process*; Markov Chain Monte Carlo*; 5.2 to p. 302; 5.3 to p. 320; 5.3.5 Theorem 5.2, Example 5.20, Proposition 5.4; 6.2-6.6; optional: 8.1-8.3.
Exams from past years are available here.
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An article on Markov and the origins of the theory of Markov chains, by Brian Hayes.
A good reference for random walks is the book: Random Walks and Electric Networks by Doyle and Snell.
References for self-avoiding walks and pivot algorithm:
Pivot algorithm simulations and more at Nathan Clisby's website.
G. Slade. Self-avoiding walks. The Mathematical Intelligencer 16:29--35, (1994). PDF file .
Chapter 9 of N. Madras and G. Slade, The Self-Avoiding Walk , Birkhäuser, Boston, (2013 reprint). Can be downloaded from UBC library.
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