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MA682 Stochastic Analysis

Lecturer: Roger Tribe

Term(s): Term 2

Commitment: 30 lectures

Assessment: Oral exam

Assumed knowledge:

Useful background: There will be links with material from several other modules: Solutions to elliptic and parabolic linear PDEs are very closely related, and students who have taken MA250 Introduction to Partial Differential Equations, MA3G1 Theory of Partial Differential Equations or MA6A2 Advanced Partial Differential Equations will be motivated for some of these connections. Although we won't lean on the material developed in ST318 Probability Theory, the examples developed here motivated a lot of the theory of martingales.

Synergies: Financial mathematicians make use of the tools developed here so that it will eventually mesh with the ideas in ST339 Introduction to Mathematical Finance (perhaps in Masters level courses).

Leads to:

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.

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


Additional Resources

Archived Pages: 2015 2017