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Stochastic Processes
MA 532
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Introduction
Stochastic processes (or random functions) are ubiquitous in modeling for engineering, biology and finance. This course proposes an in-depth introduction to the basic mechanisms describing those objects.

We begin by a review of generating functions for probability distributions. This will allow an elementary treatment of two very classical processes, namely the simple random walk and branching processes. Then we will introduce and analyze discrete time Markov chains. A chapter is also dedicated to continuous time Markov processes, another extremely important class of models. If time allows it, we will go back to convergences of ransdom sequences and martingales.
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Bibliography
  1. Grimmett, Stirzacker: Probability and Random Processes. Oxford University Press, 2020.
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Important links
Please consult the course webpage.
Here is a link to the course calendar.
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Homework
The homework will be taken from the book. Homeworks are usually due on Friday.
The assignments are given in the attached calendar.

Rules for the homework:
  1. Please resolve any error in the grading (hws and tests) WINTHIN ONE WEEK after the return of each homework and exam.
  2. No late homeworks are accepted.
  3. You are encouraged to discuss the homework problems with your classmates but all your handed-in homeworks must be your own work.
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Midterms
Our Midterm 1 will be on 2/28/24, from 3:30pm to 4:30pm.
Program: Lessons 1-20 (see calendar).
This link sends you to the Spring 24 Midterm, together with Some solutions
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Final exam
Our Final will be on 4/30/24 from 9am to 10am, in BNRG B268.
Program: Lessons 21-43 (see calendar).
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Office hours
11:30am-1pm on Monday, on Zoom.
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Syllabus
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Slides (Feedback on typos appreciated)
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Documents with computations (notes from lectures)
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