734 Theory of Probability II, Spring 2018

Meetings: TR 1-2:15, Van Vleck B139
Instructor: Timo Seppäläinen
Office: 425 Van Vleck. Office Hours: MW 11-12 and any other time by appointment
Office Phone: 263-3624
E-mail: seppalai at the department's address which is math and dot and wisc and dot and edu

This is the second semester of the 2-semester graduate level introduction to probability theory 733-734. The textbook is

Richard Durrett: Probability: Theory and Examples. Version 5 is available from Rick Durrett's homepage at Duke University.

Material covered. We continue from the point where 733 left off, and begin with review as needed. The topics we cover include Markov chains, stationary processes and the ergodic theorem, the subadditive ergodic theorem and some of its applications, Brownian motion and some of its basic properties. Suggestions for topics are also very welcome.

Prerequisites. Some familiarity with key parts from the first semester, such as measure-theoretic foundations of probability, laws of large numbers, central limit theorem, conditional expectations, and martingales.

Course grades will be based on take-home work and possibly one exam.


Spring 2015 Schedule

This is an example of what has been covered in the past. Section numbers refer to 4th edition of Durrett's book.

Week Tuesday Thursday
1 1/20-1/23 Review of conditional expectation and martingales. 5.4 Doob's inequality, Lp convergence of martingales. 5.5 Uniform integability, L1 convergence of martingales.
2 1/26-1/30 Applications of L1 convergence (S. Roch). Optional stopping (S. Roch).
3 2/2-2/6 5.6 Backward martingales. 5.6 Exchangeable processes and deFinetti's theorem.
4 2/9-2/13 Class rescheduled. Beginning Markov chains (B. Valkó).
5 2/16-2/20 Discussion: Extreme points, exchangeable measures, and Choquet's theorem. Martingale CLT. Burkholder-Davis-Gundy inequalities. 6.1-6.2 Markov chains.
6 2/23-2/27 6.3 Strong Markov property. 6.3 Reflection principle. 6.4 Recurrence and transience for countable state space.
7 3/2-3/6 6.4 Recurrence and transience for countable state space. 6.4 Martingale argument for recurrence.
8 3/9-3/13 6.5 Invariant distributions and measures. 6.5 Invariant distributions and measures.
9 3/16-3/20 6.6 Convergence theorem. Final comments on Markov chains: law of large numbers, recurrence and superharmonic functions. 7.1 Stationary processes, measure-preserving dynamical systems, ergodicity.
10 3/23-3/27 7.1 Ergodicity of an irreducible Markov chain and an irrational rotation of the circle. 7.2 Maximal ergodic lemma. 7.2 Proof of Birkhoff's ergodic theorem.
11 4/6-4/10 Ergodicity, mixing and trivial tail field. Markov chain CLT via martingale CLT and the ergodic theorem. Solving Poisson's equation for a Markov chain. 7.4 Examples of subadditive ergodicity.
12 4/13-4/17 7.4 Kingman-Liggett subadditive ergodic theorem: proof of almost sure convergence. 7.4 Kingman-Liggett subadditive ergodic theorem: proof of L1 convergence. Applications.
13 4/20-4/24 8.1 Brownian motion: definition, the Gaussian (heat) kernel, discussion about the construction of Brownian motion, Kolmogorov's criterion for path continuity. 8.1 Proof of Kolmogorov's criterion, completion of the construction of Brownian motion.
14 4/27-5/1 Lévy's construction of Brownian motion. Nondifferentiability of paths. Quadratic variation. (B. Valkó). 8.2 Markov property of Brownian motion, Blumenthal's 0-1 law, trivial tail field, Brownian filtrations. (B. Valkó).
15 5/4-5/8 8.3 Strong Markov property of Brownian motion. Hitting times. 8.6 Donsker's theorem.


Timo Seppalainen