Theory of Probability Including Measure Theory : Math 8652

Spring 2019

Welcome to the course webpage!

Course Instructor: Arnab Sen
Office: 238 Vincent Hall
Email: arnab@math.umn.edu

Class time: MW 4:00 - 5:15 pm
Location: Vincent Hall 211
Office hours: M 1:00-2:00 pm, W 11:30 - 12:30 pm, and by appointment.

Course Description: This is the second half of a yearly sequence of graduate probability theory at the measure-theoretic level. There will be an emphasis on rigorous proofs.
In this course, we aim to cover the following main topics:

Prerequisite: Math 8651.

Textbook (for both 8651 and 8652): Probability: Theory and Examples by Rick Durrett, Cambridge Series in Statistical and Probabilistic Mathematics, 4th Edition. Available online. A fifth edition of the textbook is also avaialble online.

Other recommended books:
1. Probability and Measure (3rd Edition) by Patrick Billingsley.
2. A Modern Approach to Probability theory by Bert Fristedt and Lawrence Gray.
3. Probability with Martingales by David Williams.
4. Brownian motion by Peter Morters and Yuval Peres.

Homework: There will be 6 homework assignments. The lowest score will be dropped in calculating the final score.

Final Exam: There will be a take-home final exam.

Grading: Homework 50%, Final 50%.

Canvas: Homework assignments, lecture notes and grades will be posted on Canvas.

Announcements: The take home final exam will be emailed to your UMN account before 12:00pm on Wednesday, May 8. The deadline is 11:59 am, Friday May 10.

Weekly schedule:
Jan 23 martingales, examples.
Jan 28, 30 Doob's decomposition, stopping times. No class on Jan 30.
Feb 4, 6 Doob's upcrossing inequality, martingale convergence theorem, maximal inequalities, L^p-martingale convergence theorem
Feb 11, 13 Galton-Watson Process, uniform integrability, uniform integrable martingales, Optional Stopping theorem, Gambler's ruin problem
Feb 18, 20 proof of OST, backward martingale, convergence, SLLN using backward martingale, Hewitt-Savage 0-1 law, exchangeable sequence
Feb 25, 27 de Finetti's theorem, polya's urn. Markov chains: defintion, construction and example, Markov and strong Markov property.
Mar 4, 6 Markov and strong Markov property (contd.), recurrent and transient states, characterization of recurrent and transient states for finite state Markov chain
Mar 11, 13 recurrence/transience of random walks on Z^d, existence and uniqueness of stationary measure and stationary distribution, positive and null recurrence
Mar 18, 20 spring break
Mar 25, 27 reverisble Markov chains, asymptotic density of states, periodicity, convergence to equilibrium, an introduction to mixing time of Markov chains: RW on cycle, RW on hypercube [You can find much more about the mixing time of Markov chains in this book by Levin, Peres and Wilmer].
Apr 1, 3 Measure preserving systems, stationary sequence, ergodicity, examples: i.i.d. sequence, function of stationary sequence, stationary Markov chain, rotation on circle, Birkhoff's ergodic theorem
Apr 8, 10 asymptotic frequency of a fixed pattern in Markov chain, range of random walk, Kingman's subadditive ergodic theorem, first passage percolation, longest common subsequence
Apr 15, 17 Introduction to Brownian Motion. Construction of BM. Basic properties of BM. Holder exponent of Brownian paths.
Apr 22, 24
Apr 29, May 1
May 6