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MA482 Stochastic Analysis

Lecturer: Dr. Roger Tribe

Term(s): Term 2

Status for Mathematics students: List C

Commitment: 30 lectures

Assessment: 3-hour examination

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.

Leads To:
The module complements the module MA4F7/ ST403 Brownian Motion.

Content:
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.

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

 

Additional Resources

Archived Pages: 2015 2017


yr1.jpg
Year 1 regs and modules
G100 G103 GL11 G1NC

yr2.jpg
Year 2 regs and modules
G100 G103 GL11 G1NC

yr3.jpg
Year 3 regs and modules
G100 G103

yr4.jpg
Year 4 regs and modules
G103

Archived Material
Past Exams
Core module averages