|Meetings: TR 1-2:15, Sterling 1313|
|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. The official version for the course is the one dated January 11, 2019.
Material covered. We begin with a quick review of martingales and then take up Sections 4.6 (Uniform integrability) and 4.7 (Backward martingales). The section on backward martingales gives us the opportunity to discuss exchangeable processes and de Finetti's theorem. After that we go on to Markov chains, stationary processes and the ergodic theorem, the subadditive ergodic theorem and some of its applications, and finally Brownian motion and some of its basic properties.
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.
||4.6 Uniform integability, L1 convergence.|| 4.6 L1 convergence of martingales, Lévy's 0-1 law, tail σ-algebra, Kolmogorov's 0-1 law. |
4.7 Backward martingales.
||4.7 Exchangeable σ-algebra, exchangeable probability measures.||4.7 Hewitt-Savage 0-1 law, de Finetti's theorem, exchangeable measures as mixtures of IID product measures.|
|| 4.7 de Finetti's theorem as an instance of Choquet's theorem. An application of the Hewitt-Savage 0-1 law to random walk.
5.1-5.2 Beginning Markov chains. Markov property of random walk.
Homework 1 due.
|5.1-5.2 Transition probabilities. Construction of Markov chains.|
|| Introduction to the corner growth model. |
5.2 Markov property extended to the infinite future. Chapman-Kolmogorov equations. Stopping times and strong Markov property.
|5.3 Recurrence, transience and canonical decomposition for discrete state Markov chains.|
||5.3-5.4 Recurrence and transience. 5.5 Invariant and reversible measures and distributions.|| 5.5 Existence and uniqueness of invariant measures and distributions for countable state space. Stationary processes and shift-invariant probability measures. 5.6 Periodicity. |
Homework 2 due.
|6 ||5.6 Proof of the Markov chain convergence theorem. Dissection principle. Other asymptotic results for Markov chains.||6.1 Stationary processes, measure-preserving dynamical systems, ergodicity.|
|| 6.1 Ergodicity of Markov chains in general state space. Mappings preserve stationarity and ergodicity.
Homework 3 due.
|6.2 Maximal ergodic lemma and Birkhoff's ergodic theorem. (S. Roch lectures.)|
||SPRING BREAK||SPRING BREAK|