STA 2111F: Graduate Probability I (Fall, 2003)

STA 2111F is a course designed for Master's and Ph.D. level students in statistics, mathematics, and other departments, who are interested in a rigorous, mathematical treatment of probability theory using measure theory.

Instructor: Professor Jeffrey S. Rosenthal, Department of Statistics, University of Toronto
Sidney Smith Hall, room 6024; phone (416) 978-4594;; 'jeff' at ''

Time: Wednesdays, 11-2. First class Sept 10. Last class Dec 3.

Place: Lash Miller Chemistry Labs, room 158.

Textbook: Rosenthal, J.S. (2000), A First Look at Rigorous Probability Theory. Singapore: World Scientific Publishing. Available at U of T Bookstore or from (See errata.)

Further Reading (to be held on reserve in math/stat library and/or at Gerstein):

Content: We will follow the textbook fairly closely, covering approximately the first half. Specific topics to be covered include: probability measures, the extension theorem, random variables, distributions, expectations, laws of large numbers, Markov chains. (The follow-up course, STA 2211S, will then cover the rest of the textbook, including weak convergence, characteristic functions, central limit theorems, Radon-Nykodym Theorem and Lebesgue Decomposition, conditional probability and expectation, martingales, and Kolmogorov's Existence Theorem.)

Prerequisites: Students should have a strong undergraduate background in Real Analysis, including calculus, sequences and series, elementary set theory, and epsilon-delta proofs, at the level of (say) "Elementary Classical Analysis" by Jerrold E. Marsden (W.F. Freeman and Co., 1974); or "Real Analysis with Real Applications" by K.R. Davidson and A.P. Donsig (Prentice-Hall, 2002). Some previous exposure to undergraduate-level probability theory is also recommended.

Evaluation: See separate sheet.

Note: To avoid conflict of interest, the instructor will refund $2 (his approximate royalties) to each student who purchases the textbook.

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