[ 403PTMSSDEV22 ] VL Stochastic Differential Equations 2

Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS M1 - Master's programme 1. year Mathematics Evelyn Buckwar 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Industrial Mathematics 2022W
Objectives At the end of the course, students will have developed a deepened understanding of stochastic differential equations, including knowledge about the main solution techniques and transformation methods.
Subject Solutions and moments of stochastic differential equations, transformation methods for stochastic differential equations (Lamperti transform, change of measure and Girsanov’s theorems, Stratonovich calculus), connection to partial differential equations.
Criteria for evaluation Oral exam

Methods Blackboard presentation
Language English
Study material
  • Stochastische Differentialgleichungen: Theorie und Anwendungen, L. Arnold
  • Stochastic differential equations: An introduction with applications, B. Oksendal
  • Stochastic differential equations and applications, X. Mao
Changing subject? No
On-site course
Maximum number of participants -
Assignment procedure Direct assignment