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MATH 6605 3.00AF (Fall 2010)
Probability Theory


The course will cover the probabilistic techniques required for rigorous work in probability and statistics at the graduate level. We will start by studying notions of convergence in probability theory, such as the weak law of large numbers, the Borel-Cantelli lemma, the strong law of large numbers, the law of the iterated logarithm, characteristic functions, weak convergence, and the central limit theorem.

If time permits, we will go on to other topics, which may be chosen with the interests of the class in mind. For example, we could look at weak convergence of stochastic processes and Donsker's theorem (topics essential for the study of empirical processes in statistics, or for constructing Brownian motion).


Undergraduate probability and mathematical analysis. Measure theory is an asset, but is not required.

Course Webpage:

Instructor/Contact Information:

Tom Salisbury Department of Mathematics and Statistics


MWF 9:30-10:20, in BC 228

Office hours:

Wed 2-3, Fri 11-12

I will try to post a notice on the course webpage if other commitments make it necessary to reschedule one or more office hour. If you need to see me outside these hours, you are welcome to e-mail or call me to try to arrange an appointment.



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