Math/Stat 733 - Theory of Probability I.
Fall 2020
Meetings: TR 1pm-2:15pm (online)
Instructor: Benedek Valkó
Email: valko at math dot wisc dot edu
Office hours: Tu 4-5pm, F 3-4pm, or by appointment (online)
This
page gives an overview of the course, all relevant
information and resources will be provided in the official
Canvas page.
Course description
This is the first semester of a two-semester graduate-level
introduction to probability theory and it also serves as a
stand-alone introduction to the subject. The course will focus on
the basics of probability and cover at least the following topics:
foundations (probability spaces and existence of processes),
independence, zero-one laws, laws of large numbers, weak convergence
and the central limit theorem, conditional expectations and their
properties, and martingales (convergence theorem and basic
properties).
Textbook
Richard Durrett: Probability:
Theory and Examples, 5th edition, 2019
There are several good textbooks on probability and it might help to
have a look around. Here is a list of textbooks that could be used
for extra reading:
- Olav Kallenberg: Foundations of Modern Probability. 2nd
edition, Springer, 2002
- William Feller. An introduction to probability
theory and its applications. Vol. I. Third edition. John Wiley
and Sons Inc., New York, 1968.
- David Williams. Probability with martingales.
Cambridge Mathematical Textbooks. Cambridge University Press,
Cambridge, 1991.
- Patrick Billingsley. Probability and measure.
Wiley Series in Probability and Mathematical Statistics. John
Wiley & Sons Inc., New York, 1995.
Prerequisites
Measure
theory is a basic tool for this course. A suitable background
can be obtained from Math 629 or Math 721. Chapter 1 in Durrett
covers the measure theory needed. We will very briefly review
some measure theory at the beginning of the semester, but it
will be expected that all students in the course are familiar
with the basics. Prior exposure to elementary probability theory
could be useful, but it is not required.
Course content
We cover selected
portions of Chapters 1-4 of Durrett. This is a rough course
outline:
Weeks 1-2: Foundations, properties of probability spaces
|
Weeks 3-5: Independence, 0-1 laws, strong law of large
numbers |
Weeks 6-10: Characteristic functions, weak convergence and
the central limit theorem |
Weeks 11-15: Conditional expectation, Martingales
|
The course continues in the spring semester as Math 734 covering
topics such as Markov chains, stationary processes, ergodic
theory, and Brownian motion.
Evaluation
Course grades will be based on biweekly home work assignments
(25%), class participation (15%), a midterm exam (30%) and the
final exam (30%). (See the Canvas page for more information.)
Piazza
We will be using Piazza for class
discussion. The system is catered to getting you help fast
and efficiently from classmates and myself. Rather than
emailing all questions to me, I encourage you to post your
questions on Piazza. If you have any problems or feedback for the
developers, email team@piazza.com.
You can access our Piazza page from the Canvas page of the course.
Instructions for homework
assignments
- Assignments will be posted and collected on the Canvas page.
- No late assignments will be accepted.
- Observe rules of academic integrity. Handing in
plagiarized work, whether copied from a fellow student or off
the web, is not acceptable. Plagiarism cases will lead to
sanctions.
- Neatness and clarity are essential. Write one problem per page
except in cases of very short problems. Staple you sheets
together. You are strongly encouraged to use LaTeX (or Overleaf) to typeset your solutions.
- It is not trivial to learn to write solutions. You have to
write enough to show that you
understand the flow of ideas and that you are not jumping to
unjustified conclusions, but not
too much to
get lost in details. If you are unsure of the appropriate level
of detail to include, you can separate some of the technical
details as "Lemmas" and put them at the end of the solution. A
good rule of thumb is if
the grader needs to pick up a pencil to check your assertion,
you should have proved it. The grader can
deduct points in such cases.
- You can use basic facts from analysis and measure theory in
your homework, and the theorems we cover in class without
reproving them. If you find a helpful theorem or passage in
another book, do not copy the passage but use the idea to write
up your own solution. If you do use other literature for help,
cite your sources properly. However, it is better to attack the
problems with your own resources instead of searching the
literature or the internet. The purpose of the homework is to
strengthen your problem solving skills, not literature search
skills.
- It is valuable to discuss ideas for homework problems with
other students. But it is not acceptable to write solutions
together or to copy another person's solution. In the end you
have to hand in your own personal work. Similarly,
finding solutions on the internet is tantamount to cheating. It
is the same as copying someone else's solution.
If you would like to learn more
probability...
Check out the Probability
Seminar, the Graduate
Probability Seminar and the Statistics
Seminar for talks that might
interest you. Have a look at the wiki
page of our probability group to learn more about the
probabilists at UW-Madison. Consider attending the 2020 Midwest
Probability Colloquium (this will be held online this year).