Information

Thursdays, 9am-10:50am, Warren Weaver Hall 1302

The most up-to-date lecture notes and homework assignments will be posted to the class Piazza page. Registered students can access this page via the link on the NYU Classes page. Students who wish to audit the class, should write to the instructor to request access to the Piazza page.

Prerequisites: Basic Probability (or equivalent masters-level probability course), and good upper level undergraduate or beginning graduate knowledge of linear algebra, ODEs, PDEs, and analysis.

Description: This course will introduce the major topics in stochastic analysis from an applied mathematics perspective. Topics to be covered include Markov chains, stochastic processes, stochastic differential equations, numerical algorithms for solving SDEs and simulating stochastic processes, forward and backward Kolmogorov equations. It will pay particular attention to the connection between stochastic processes and PDEs, as well as to physical principles and applications. The class will attempt to strike a balance between rigour and heuristic arguments: it will assume that students have seen some analysis before, but most results will be derived without using measure theory. The target audience is PhD students in applied mathematics, who need to become familiar with the tools or use them in their research.

The course will be divided roughly into two parts: the first part will focus on stochastic processes, and the second part will focus on stochastic differential equations and their associated PDEs.

Homework will be a critical part of the course. Lectures will mostly be theory, and examples or extensions will be assigned as homework problems. You must do these if you want to learn something from the course. Homework will require some computing, preferably in Python or Matlab. Students without programming experience will have to put in extra effort in the first few weeks.

References

There are three textbooks that are not required, but that are highly recommended:
  • G. A. Pavliotis, Stochastic Processes and Applications.
  • G. Grimmett and D. Stirzaker, Probability and Random Processes. (This is the textbook for Basic Probability)
  • C. Gardiner, Stochastic Methods: A Handbook for the Natural and Social Sciences.
You can download the first one for free from the Springer website if you are a member of NYU. It will also be available at the bookstore.

Other good references include:
  • B. Oksendal, Stochastic Differential Equations
  • L. Koralov and Y. G. Sinai, Theory of Probability and Random Processes
  • R. Durrett, Essentials of Stochastic Processes
  • R. Durrett, Stochastic Calculus: A Practical Introduction
  • I. Karatzas and S. E. Shreve, Brownian Motion and Stochastic Calculus
  • L. Arnold, Stochastic Differential Equations: Theory and Applications
  • P. Kloeden and E. Platen, Numerical Solution of Stochastic Differential Equations
  • Breiman, Probability

Lecture Notes

Updated lecture notes are available here .

Thanks to the students of ASA 2019 and previous years' courses, for finding typos/mistakes in these notes. These notes are continuously evolving, so please let me know if you find other mistakes in them.