Module MA4615-KP05

Duration

1 Semester

Turnus of offer

irregularly

Credit points

5

Course of studies, specific fields and terms:

• Master CLS 2023, optional subject, mathematics
• Bachelor CLS 2023, optional subject, mathematics
• Bachelor CLS 2016, optional subject, mathematics
• Master CLS 2016, optional subject, mathematics

Classes and lectures:

• Numerical methods for stochastic processes (lecture, 2 SWS)
• Numerical methods for stochastic processes (exercise, 1 SWS)

Workload:

• 20 hours exam preparation
• 45 hours in-classroom work
• 85 hours private studies and exercises

Contents of teaching:

• Basic principles of stochastic processes in continuous time
• Stochastic differential equations
• Discrete time approximations for solutions of stochastic differential equations
• Numerical schemes for strong and weak approximations

Qualification-goals/Competencies:

• To impart basic principles of stochastic processes and of some numerical schemes
• To learn methods of proof as well as the application of algorithms
• Accomplished handling of essential concepts and results as well as of selected advanced topics

Grading through:

• Written or oral exam as announced by the examiner

Requires:

• Stochastic processes (MA4610-KP05)
• Stochastics 2 (MA4020-KP07)
• Stochastics 1 (MA2510-KP04, MA2510)

Responsible for this module:

• Prof. Dr. rer. nat. Andreas Rößler

Teacher:

• Institute for Mathematics
• Prof. Dr. rer. nat. Andreas Rößler

Literature:

• P. E. Kloeden, E. Platen : Numerical Solution of Stochastic Differential Equations Springer-Verlag, Berlin, 1999
• P. E. Kloeden, E. Platen, H. Schurz : Numerical Solution of SDE Through Computer Experiments Springer-Verlag, Berlin, 2003
• G. N. Milstein, M. V. Tretyakov : Stochastic Numerics for Mathematical Physics Springer-Verlag, Berlin, 2004

Language:

• English, except in case of only German-speaking participants

Notes:

Last Updated:

22.02.2022