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MA682 Stochastic Analysis

Lecturer: Dr. Sebastian Vollmer

Term(s): Term 2

Commitment: 30 lectures

Assessment: Oral exam

Prerequisites: A willingness, even an enthusiasm, to work with random variables is the key prerequisite. No single module is a prerequisite. Earlier probability modules will be some use. The framework is measure theory, so it is a nice illustration of the ideas from MA359 Measure Theory, or ST342 Maths of Random Events, or ST318 Probability Theory. The content will also link with some content from modules on ODE's and PDEs. A student without any of the above would have to work hard.

We will introduce stochastic integration, and basic tools in stochastic analysis including Ito’s formula. We will also introduce lots of examples of stochastic differential equations.

Laurence Evans: An introduction to Stochastic Differential Equations.
Bernt Oksendall: Stochastic Differential Equations.


Additional Resources

Archived Pages: 2015 2017