Math 318, Jan-Apr 2017, G. Slade

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 Saraí Hernández-Torres in BUCH B216: 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.

Regular office hours will cease on Thursday April 6. There will be an office hour on Wednesday April 19, 15:00-16:00 in MATX 1102.

Octave resources are available here. You should instal Octave on your computer as soon as possible (or MATLAB if you prefer).

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) download the files gasquantities.mat and gasquantities2.mat Solutions
Assignment 8 (out Mar 10, due Mar 17) tutorial on linear regression Solutions
Test 2 (Mar 22) Solutions
Assignment 9 (out Mar 24, due Mar 31) download the file matrixEhr.m 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:
permutations and combinations*; 1.1-1.5; 2.1-2.5; 5.2 pp. 278, 280-281, 287-288; Poisson process* and 5.3.3; characteristic functions* and 2.6 to p. 64; 2.8; statistics*; 4.5.1; random walk* and Example 4.18; 3.2-3.4 to p. 103; 4.1-4.3 to p. 201; 4.4 to p. 207; 4.4.1; 4.7-4.8 to p. 242; Markov Chain Monte Carlo*.
For the 10th edition:
permutations and combinations*; 1.1-1.5; 2.1-2.5; 5.2 pp. 292, 294-295, 302; Poisson process* and 5.3.3; characteristic functions* and 2.6 to p. 69; 2.8; statistics*; 4.5.1; random walk* and Example 4.18; 3.2-3.4 to p. 110; 4.1-4.3 to p. 211; 4.4 to p. 217; 4.4.1; 4.7-4.8 to p. 255; Markov Chain Monte Carlo*.

Exams from past years are available here.

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On tests and the final exam you will be provided with tables and, when appropriate, Student-T table.
Example for the Student-T table: If T has 3 degrees of freedom then P(T<2.353)=0.95 and P(|T|<2.353) = 0.9.

References for self-avoiding walks:
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, (1996) (can be downloaded from UBC library).
More advanced: N. Clisby, Accurate estimation of the critical exponent nu for self-avoiding walks via a fast implementation of the pivot algorithm, Physical Review Letters 104:055702, February 5, 2010.

Interesting animations demonstrating the central limit theorem.

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.

Recommended, fun, and accessible general reading about probability: Struck by Lightning by J.S. Rosenthal, and The Improbability Principle by D.J. Hand.

Gaussian distribution on the German 10 mark note.

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