Math 632 - Introduction to Stochastic Processes, Lecture 3
Meetings: TuTh 11PM-12:15PM Van Vleck B239
Instructor: Benedek Valkó
Office: 409 Van Vleck
Email: valko at math dot wisc dot edu
Office hours: W 3:40pm-5pm (via Zoom), Th 12:15pm-12:55pm
(in person, B329) or by appointment
This is the course homepage. Part of this information is repeated
in the course syllabus that you find on Canvas. Important deadlines
from the Registrar's page.
Math 632 is a course on basic stochastic processes and
applications with an emphasis on problem solving. Topics will
include discrete-time Markov chains, Poisson point
processes, continuous-time Markov chains, and renewal processes.
In class we go through theory, examples to illuminate the
theory, and techniques for solving problems. Homework exercises
and exam problems are paper-and-pencil calculations with examples
and special cases, together with short proofs.
A typical advanced math course follows a strict theorem-proof
format. 632 is not of this type. Mathematical theory is discussed
in a precise fashion but only some results can be rigorously
proved in class. This is a consequence of time limitations and the
desire to leave measure theory outside the scope of this course.
Interested students can find the proofs in the literature. For a
thoroughly rigorous probability course students should sign up for
the graduate probability sequence Math/Stat 733-734 which requires
a background in measure theory from Math 629 or 721. An
undergraduate sequel to 632 in stochastic processes is Math 635 -
Introduction to Brownian motion and stochastic calculus.
Rick Durrett: Essentials of Stochastic Processes. 3rd
edition. We expect to cover parts of Chapters 1-5. UW-Madison
students can download this textbook for free through SpringerLink.
Separate lecture notes will also be provided on Canvas.
Other textbooks which could be used for supplemental reading:
- Greg Lawler: Introduction to Stochastic Processes, Chapman
- Sidney Resnick: Adventures in Stochastic Processes,
- Sheldon Ross: Stochastic Processes, Wiley
- Sheldon Ross: Introduction to Probability Models,
The official prerequisites are an introductory probability course
(Math 309/Stat 311/Math 431/Math 531) and a course in linear
algebra or intro to proofs (Math 320/340/341/375/421).
It is important to have a good knowledge of undergraduate
probability. This means familiarity with basic probability models,
random variables and their probability mass functions and
distributions, expectations, joint distributions, independence,
conditional probabilities, the law of large numbers and the
central limit theorem. If you wish to acquire a book for review,
the Math 431 textbook Introduction
to Probability by Anderson, Seppäläinen and Valkó is
Here is a rough outline for the course:
Week 1: Quick probability review, renewal processes
Weeks 2-6: Discrete time Markov Chains
Week 7-8: Martingales
Week 9-10: Poisson process
Week 11: Renewal processes
Week 12-15: Continuous Time Markov Chains
We cover Chapters 1-5 of Durrett's book (but not necessarily in the
All course materials (syllabus, lecture notes, homework assignments,
solutions etc.) will be posted on the Canvas site of the course.
Piazza is an online platform for class
discussion. Post your math questions on Piazza and answer other
students' questions. Our class Piazza page can be accessed from
the Canvas page of the course.
Instructions for homework
Homework will be assigned weekly or biweekly. The assignments will
be posted on Canvas, and they will have to be submitted in Canvas
as a single PDF file.
- 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. You are strongly
encouraged to use LaTeX to typeset your solutions. (Check out
Overleaf for an online tool to edit LaTeX documents.)
Handwritten solutions will need to be scanned, it is your
responsibility to check that the scanned file is in PDF format,
and your work is legible.
- Make sure to properly justify your solutions. Computations
without appropriate explanation will not receive credit, even if
the final numerical answer is correct.
- You can use basic facts from probability in your homework and
the results we cover in class. 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
- 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.