Theory of Probability Including Measure Theory : Math 8651

Fall 2018

Welcome to the course webpage!

Course Instructor: Arnab Sen
Office: 238 Vincent Hall

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

Course Description: This is the first 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: Upper division analysis: Math 5616 (or equivalent) - the students should be familier with concepts such as uniform convergence, continuity, sequences and series of numbers and functions, Riemann integral and the topology (open, closed, compact sets, etc.) of the real line. No background in measure theory will be assumed. Some familiarity with basic undergrad probability will be helpful.

Textbook (for both 8651 and 8652): Probability: Theory and Examples by Rick Durrett, Cambridge Series in Statistical and Probabilistic Mathematics, 4th Edition. Also available online.

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

Homework: There will be biweekly homework assignments (around 7 in total). The lowest score will be dropped in calculating the final score. Homework are due on the corresponding deadlines in class. Late homework will not be accepted. You are allowed and even encoraged to discuss homework solutions with your friends. However, you have to write your own solutions. To get full credit, be neat and answer with reasons.

Final Exam: 4:00-6:00pm, Monday, December 17.

Grading: Homework 60%, Final 40%.


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

Weekly schedule

Sep 5: Sigma-field, measure, Lebesgue measure
Sep 10, 12: Dynkin's π-λ theorem, Carathéodory's Extension Theorem, random variables.
Sep 17, 19: Distributions, CDF, Lebesgue integration, MCT
Sep 24, 26: DCT, change of variable formula, inequalities, product measures i
Oct 1, 3: product measure, Fubini, independence, sum of independent random variables
Oct 8, 10: Weak law of large numbers, Weierstrass approximation, coupon collector, Borel-Cantelli Lemmas and its applications
Oct 15, 17: Strong law of large numbers, Renewal theorem, Glivenko-Cantelli Theorem, Kolmogorov's 0-1 law
Oct 22, 24 : Kolmogorov's maximal inequality, convergence of random series, weak convergence and its properties, Helly's seclection theorem, tightness
Oct 29, 31: Prokhorov's theorem, central limit theorem, proof of CLT using Lindeberg's replacement lemma,
Nov 5, 7: Lindeberg-Feller CLT, examples, characteristic function, inversion formula
Nov 12, 14: continuity theorem, CLT via characteristic function, weak convergence via method of moments
Nov 19, 21: Poisson convergence, weak convergence in R^d
Nov 26, 28: Multivariate CLT, Hoeffding's inequality, conditional expectation
Dec 3, 5: properties of conditional expectation, Radon-Nikodym theorem
Dec 10*, 12*: proof of Radon-Nikodym theorem, regular conditional distribution, first and second moment method.
* Not included in the final exam.