734 Theory of Probability II, Spring 2015

Meetings: TR 1-2:15, Van Vleck B131
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 831-832. The textbook is

Richard Durrett: Probability: Theory and Examples. 4th Edition.

We begin with a quick review of conditional expectations and martingales. The first order of business is to go over what remains from Chapter 5 on martingales after the fall term. Next follow Markov chains (Chapter 6), stationary processes and the ergodic theorem, including the subadditive ergodic theorem and some of its applications (Chapter 7), and finally Brownian motion and some of its basic properties (Chapter 8). If it looks like a topic you are interested in is not touched then let me know and we can try to work it in.

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

(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